Peer Grouping and Performance Measurement to … · PEER GROUPING AND PERFORMANCE MEASUREMENT TO ... Sherman-Denison—Texoma Area Paratransit System ... Appendix D. Case Study …

Technical Report Documentation Page 1. Report No. FHWA/TX-11/0-6205-1

2. Government Accession No.

3. Recipient's Catalog No.

4. Title and Subtitle PEER GROUPING AND PERFORMANCE MEASUREMENT TO IMPROVE RURAL AND URBAN TRANSIT IN TEXAS

5. Report Date September 2010 Published: May 2011 6. Performing Organization Code

7. Author(s) Jeffrey Arndt, Suzie Edrington, Matthew Sandidge, Luca Quadrifoglio, and Judy Perkins

8. Performing Organization Report No. Report 0-6205-1

9. Performing Organization Name and Address Texas Transportation Institute The Texas A&M University System College Station, Texas 77843-3135

Prairie View A&M University Prairie View, Texas 77446

10. Work Unit No. (TRAIS) 11. Contract or Grant No. Project 0-6205

12. Sponsoring Agency Name and Address Texas Department of Transportation Research and Technology Implementation Office P.O. Box 5080 Austin, Texas 78763-5080

13. Type of Report and Period Covered Technical Report: September 2008–August 2010 14. Sponsoring Agency Code

15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Benchmarking and Improving Texas Rural Public Transportation Systems URL: http://tti.tamu.edu/documents/0-6205-1.pdf 16. Abstract Rural and small urban transit systems in Texas will become even more important with predicted changes in population trends. Rural demographic trends indicate growth in the number of persons age 65 and over coupled with a decrease in population density. Small urban area trends indicate substantial population growth and broadened geographic boundaries, yet resources to provide rural and small urban transit are limited. Therefore, transit managers find it is increasingly important to maximize service efficiency and effectiveness. The purpose of this research was to identify peer groups, performance benchmarks, and strategies used by successful transit providers to achieve high performance. The research project identifies peer groups based on the transit environment within which each agency operates, so that agencies can be compared to other operators who face similar environments. Peer group effectiveness and efficiency performance are examined within and between rural and urban peer groups, and high performers are identified for case studies. Through the case studies, key attributes are identified for achieving high operating efficiency and/or effectiveness. Performance strategies are categorized to provide transit providers with transferrable information to improve performance and increase the return on transit investment. 17. Key Words Benchmarking, Peer Analysis, Public Transportation, Performance Measurement

18. Distribution Statement No restrictions. This document is available to the public through NTIS: National Technical Information Service Alexandria, Virginia 22161 http://www.ntis.gov

19. Security Classif.(of this report) Unclassified

20. Security Classif.(of this page) Unclassified

21. No. of Pages

146

22. Price

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized

PEER GROUPING AND PERFORMANCE MEASUREMENT TO IMPROVE RURAL AND URBAN TRANSIT IN TEXAS

by

Jeffrey Arndt, Research Scientist Suzie Edrington, Research Specialist

Matt Sandidge, Assistant Transportation Researcher Luca Quadrifoglio, Ph.D., Associate Research Engineer

Texas Transportation Institute

and

Judy Perkins, Ph.D., Department Head, Civil and Environmental Engineering Department Prairie View A&M University

Report 0-6205-1 Project 0-6205

Project Title: Benchmarking and Improving Texas Rural Public Transportation Systems

Performed in cooperation with the Texas Department of Transportation

and the Federal Highway Administration

September 2010 Published: May 2011

TEXAS TRANSPORTATION INSTITUTE The Texas A&M University System College Station, Texas 77843-3135

v

DISCLAIMER

This research was performed in cooperation with the Texas Department of Transportation (TxDOT) and the Federal Highway Administration (FHWA). The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of FHWA or TxDOT. This report does not constitute a standard, specification, or regulation.

vi

ACKNOWLEDGMENTS

This project was conducted in cooperation with TxDOT and FHWA. The authors acknowledge the support and guidance of the TxDOT program coordinator Karen Dunlap of the TxDOT Public Transportation Division; David Merritt, the project director and TxDOT public transportation coordinator; and members of the Project Monitoring Committee: Alfredo Gonzales and Tamara Cope, the TxDOT public transportation coordinators. The authors appreciate the assistance of TxDOT Research and Technology Implementation representatives Duncan Stewart, Sylvia Medina, and Frank Espinosa. The authors thank Yonggao Yang, assistant professor, of Prairie View A&M University for the literature review and Yao Xing, civil engineering graduate student, of Texas A&M University for assistance in the peer group cluster analysis and peer group recommendation. The authors also thank Lisa Patke of the Texas Transportation Institute for providing assistance in document preparation. Throughout the project, representatives of the agencies that provide public transportation in Texas gave information and responded to fact-finding questions. Specifically, the authors thank Gerald Payton of Panhandle Community Services; Lynda Woods Pugh of the Ark-Tex Council of Governments; Lisa Cortinas of the Golden Crescent Regional Planning Commission; Gary Rushing and Sandra Webb of the Heart of Texas Council of Governments; Norma Zamora of the Brownsville Urban System; Noel Hernandez of the Concho Valley Transit District; and Brad Underwood of the Texoma Area Paratransit System.

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TABLE OF CONTENTS

Page List of Figures.......................................................................................................................... ix List of Tables .............................................................................................................................x

Chapter 1: Introduction ............................................................................................................1 A Present and Future Challenge ..............................................................................................2

Organization of the Research Report .......................................................................................4

Chapter 2: Transit Peer Grouping and Performance Tracking by Other States ...................5 Governmental Stance on Funding Small Urban and Rural Transit ...........................................5 Section 5307 and Section 5311 Grant Programs ......................................................................6

Performance Measurement, Peer Groups, and Benchmarking ..................................................9 Performance Measures.........................................................................................................9

Benchmarking ................................................................................................................... 11 Peer Grouping ................................................................................................................... 12

Empirical State of the Practice: Section 5307 and Section 5311 Grant Programs ................... 13

Chapter 3: Clustering Analysis............................................................................................... 17 Introduction to the Rural Clustering Analysis Process ........................................................... 17 Clustering Analyis Process .................................................................................................... 19

Analysis of Results ................................................................................................................ 20 Urban System Clustering Analysis ........................................................................................ 24

Rural and Urban Peer Grouping Summary............................................................................. 29

Chapter 4: Performance Measures......................................................................................... 35 Effectiveness and Efficiency Measures by Peer Group .......................................................... 35

Comparison of Average Operating Effectiveness and Efficiency by Peer Group ................ 35

Rural Transit District Effectiveness and Efficiency Measures by Peer Group ..................... 37 Urban Transit District Effectiveness and Efficiency Measures by Peer Group .................... 45

Comparison of Operating Effectiveness and Efficiency across Transit Districts ..................... 53

Chapter 5: Case Studies of Efficient and Effective Agencies................................................. 57 Description of Case Studies ................................................................................................... 57

Panhandle Community Services (High Operational Effectiveness)..................................... 57

Heart of Texas Council of Governments (High Operational Efficiency) ............................. 59 Ark-Tex Council of Governments (High Operational Effectiveness and Efficiency) .......... 62

Golden Crescent Regional Planning Commission (High Operational Efficiency) ............... 65 Brownsville Urban System (High Effectiveness) ............................................................... 67

San Angelo—Concho Valley Transit District (High Efficiency) ........................................ 69 Sherman-Denison—Texoma Area Paratransit System (High Efficiency) ........................... 71

Texas Transit District Strategies That Impact Operating Effectiveness and Efficiency ........... 72 Efforts to Grow Ridership—Improve Effectiveness ........................................................... 73

Efforts to Manage Costs—Improve Efficiency................................................................... 73 Efforts to Decrease Vehicle Miles and Maximize Labor Productivity—Improve Efficiency and Effectiveness ............................................................................................................... 73 Efforts to Improve Administration—Improve Effectiveness and Efficiency ....................... 74

References................................................................................................................................ 75

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Appendix A. Alternative Rural Cluster Analysis Data Detail ............................................... 79 Appendix B. Alternative Urban Cluster Analysis Data Detail ............................................ 119

Appendix C. Effectiveness and Efficiency Measures by Transit District ............................ 131 Appendix D. Case Study Fact-Finding Questions ................................................................ 133

ix

LIST OF FIGURES

Page Figure 1. FTA Section 5311 Rural Transit Funding Apportionments to Texas. ...........................1 Figure 2. Scatter Matrix of Variables. ....................................................................................... 18

Figure 3. Density versus Metro Region..................................................................................... 21 Figure 4. Rural Peer Grouping Transit Environmental Factors. ................................................. 32

Figure 5. Urban Peer Grouping Transit Environmental Factors. ................................................ 33 Figure 6. Peer Group Average Operating Effectiveness and Efficiency. .................................... 36

Figure 7. Rural Peer Group 1—Effectiveness and Efficiency Measures. ................................... 38 Figure 8. Rural Peer Group 2—Effectiveness and Efficiency Measures. ................................... 40

Figure 9. Rural Peer Group 3—Effectiveness and Efficiency Measures. ................................... 41 Figure 10. Rural Peer Group 4—Effectiveness and Efficiency Measures. ................................. 43

Figure 11. Rural Peer Group 5—Effectiveness and Efficiency Measures. ................................. 44 Figure 12. Urban Peer Group 1—Effectiveness and Efficiency Measures. ................................ 46

Figure 13. Urban Peer Group 2—Effectiveness and Efficiency Measures. ................................ 48 Figure 14. Urban Peer Group 3—Effectiveness and Efficiency Measures. ................................ 49

Figure 15. Urban Peer Group 4—Effectiveness and Efficiency Measures. ................................ 51 Figure 16. Limited Eligibility Urban Peers—Comparison to Peer Group Average. ................... 52

Figure 17. Rural Transit District Operating Effectiveness and Efficiency. ................................ 53 Figure 18. Urban Transit District Operating Effectiveness and Efficiency. ............................... 54

Figure 19. Case Study Service Area and Location. ................................................................... 56 Figure 20. Golden Crescent Regional Planning Commission Transit Providers. ........................ 66

Figure 21. Brownsville Downtown Transit Terminal. ............................................................... 68 Figure 22. San Angelo TRANSA Fixed-Route Map. ................................................................ 70

x

LIST OF TABLES

Page Table 1. Rural Transit District with Fixed-Route or Flex-Route Services. ...................................2 Table 2. FTA Formula Grant Authorizations FY 2005–FY 2009. ...............................................6

Table 3. Federal-Aid Transit Programs (Formula Funded). .........................................................7 Table 4. Peer Grouping and Performance Measures. ................................................................. 14

Table 5. Use of Peer Groups. .................................................................................................... 14 Table 6. Usage of Performance Measures. ................................................................................ 15

Table 7. Composite Peer Grouping and Performance Measures State of the Practice. ............... 15 Table 8. Correlation Coefficients between Variables. ............................................................... 19

Table 9. ANOVA for C2 without HHs without Autos or Population below Poverty Level. ....... 22 Table 10. Final Rural Cluster Results/Peer Groups. .................................................................. 23

Table 11. ANOVA Tests of Urban Cluster Alternatives............................................................ 24 Table 12. Final Urban Cluster Results/Peer Groups. ................................................................. 28

Table 13. Rural Peer Groupings and Environmental Data Elements. ......................................... 29 Table 14. Urban Peer Groupings and Environmental Data Elements. ........................................ 31

Table 15. Urban Peer Group—Limited Eligibility Providers. .................................................... 32 Table 16. Peer Group Comparison of Operating Effectiveness and Efficiency. ......................... 36

Table 17. Rural Peer Group 1—Effectivness and Efficiency Measures. .................................... 37 Table 18. Rural Peer Group 2—Effectiveness and Efficiency Measures. .................................. 39

Table 19. Rural Peer Group 3—Effectiveness and Efficiency Measures. .................................. 41 Table 20. Rural Peer Group 4—Effectiveness and Efficiency Measures. .................................. 42

Table 21. Rural Peer Group 5—Effectiveness and Efficiency Measures. .................................. 44 Table 22. Urban Peer Group 1—Effectiveness and Efficiency Measures. ................................. 46

Table 23. Urban Peer Group 2—Effectiveness and Efficiency Measures. ................................. 48 Table 24. Urban Peer Group 3—Effectiveness and Efficiency Measures. ................................. 49

Table 25. Urban Peer Group 4—Effectiveness and Efficiency Measures. ................................. 50 Table 26. Limited Eligibility Urban Peers—Effectiveness and Efficiency Measures. ................ 52

Table 27. HOTCOG Fiscal Year 2009 Purchase of Service Expenditures by Subcontractor. ..... 61 Table 28. Ark-Tex Council of Governments Major Generators of Service by County. .............. 63

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CHAPTER 1: INTRODUCTION

Rural transit is the lifeblood of millions of Americans living in non-urbanized areas. Research published by the Transportation Research Board demonstrates that rural transit systems that succeed in serving commute and medical trips need to generate a cost-to-benefit ratio on the federal investment of 3.35. The Safe, Accountable, Flexible, Efficient Transportation Equity Act—A Legacy for Users (SAFETEA-LU) included substantial increases for rural transit in recognition of the unmet needs of those communities. Figure 1 displays FTA Section 5311 Non-Urbanized (Rural) apportionments for Texas from fiscal years (FY) 1999 to 2008. The jump between 2005 and 2006 reflects the SAFTEA-LU funding increase.

Figure 1. FTA Section 5311 Rural Transit Funding Apportionments to Texas.

The majority of Texas’ 38 rural transit districts operate demand-response (DR) service; that is, passengers schedule individual rides from specific origins to specific destinations. A vehicle picks up passengers at their origin, usually curbside, and ultimately delivers them to their destination. However, a passenger may share the ride (or a portion of the ride) with another customer. DR services are inherently less productive than fixed-route services, further challenging rural providers to meet growing demand. A few rural transit districts operate fixed-route (FR) service. FR services run along a pre-established route and stop at pre-established stops pursuant to a published schedule. In rural settings, these fixed-route services are often commuter or express services and may require that customers drive/ride to a fixed stop each morning to catch a non-stop ride to their work location. In some cases, drivers are allowed to deviate from the route slightly to pick up or drop off passengers, a practice often termed flex routing. Table 1 lists rural transit districts offering some fixed-route services in their region.

$0$5,000,000

$10,000,000$15,000,000$20,000,000$25,000,000$30,000,000$35,000,000

FY99FY00

FY01FY02

FY03FY04

FY05FY06

FY07FY08

Fiscal Year

2

Table 1. Rural Transit District with Fixed-Route or Flex-Route Services. Rural Transit District Any DR? Services Brazos Transit District Yes Commuter service Capital Area Rural Transportation System Yes Fixed-route local services in San Marcos and Bastrop Cleburne Yes Regional express service Colorado Valley Yes Local and/or commuter service (8 locations) El Paso County No Commuter service Fort Bend County Yes Commuter service South Padre Island No Circulator Webb County Community Action Agency Yes Regional express service

Along with diversity of service type, the rural districts vary significantly in other respects. The geographic extent of districts ranges from compact areas like El Paso County and South Padre Island to the expansive area covered by West Texas Opportunities to the west and Brazos Transit District to the east.

A PRESENT AND FUTURE CHALLENGE

Rural transit in Texas will become even more important over the next 20 to 30 years according to demographic trends. The State Demographer’s Office generated projections that indicate the following among statewide trends:

Aging. As the Baby Boomers continue aging and longevity increases, the percentage of the population that is age 65 or over is expected to grow nearly 300 percent over the next 30 years. This will likely also lead to a large increase in the numbers of people with physical or cognitive conditions that preclude them from driving.

Rural retirement. Projections indicate that as people retire, they are expected to leave the large urban centers and settle in the rural areas of the state.

Rural population and density. Although total rural population in Texas is increasing because counties near metropolitan areas and along the border are growing rapidly, the percentage of the state’s population residing in rural areas is expected to decrease over time. In counties in west Texas, the Panhandle, and some counties south of San Antonio, population is declining and migration of seniors is not expected to increase the density of population in rural areas.

In combination, these trends indicate that rural transit providers will face an increase in demand based on demographics. However, they will be challenged to maintain the service effectiveness (passengers per revenue mile) with decreasing population density. In order to meet rising demand, they will need to provide the most efficient service possible, maximizing the miles of service they provide for each dollar they spend (revenue miles per operating cost). These two factors—passengers per revenue miles and revenue miles per operating cost—also play a role in the amount of federal and state rural funding each provider receives. Rural providers are allocated funds based on relative need and performance. Need is calculated based on weighted population (75 percent) and land area (25 percent); performance is based on equally weighted local contribution per operating expense, passenger per revenue miles, and revenue

3

miles per operating expense. The funding calculation is weighted 65 percent based on need and 35 percent based on performance. Both need and performance are allocated based on an individual agency’s relative position among all rural providers. Each year, the average value of each performance indicator may change. If that average improves, then in order to maintain the same share of funding, an agency must also improve at the same rate. Since the Texas Transportation Commission updated the funding formula in 2006, rural providers consistently have sought to understand how to interpret their performance indicators, both in terms of a common standard and within the context of other providers. The only peer group that existed was the set of all rural providers. Understandably, many providers wanted to compare their performance to a smaller subset of providers. The original purpose of this research was to develop peer groups and performance benchmarks as a tool to improve service effectiveness and efficiency for rural transit providers. As researchers shared preliminary information on the formation of rural peer groups, state-funded urban transit providers expressed interest in a similar effort on their systems. These urban systems are facing substantial population growth and broadened geographic boundaries. An expected increase in the number of urbanized areas within the state after Census 2010 will cause funding to be spread even further. The allocation of state funds to urban transit providers is based on needs and performance, similar to the way rural funding is allocated. The state-funded urban allocation formula differs from the rural allocation formula in the following key respects:

A portion of the funding is used to support transit in four systems in the Dallas-Fort Worth area that serve only seniors and persons with disabilities (limited eligibility providers).

In addition to the three performance indicators used to allocate rural transit funding, state-funded urban systems have a fourth indicator—passenger boardings per capita. This indicator would benefit agencies that serve substantial numbers of non-residents such as college students and tourists. Unlike for rural funding allocation, the four performance indicators used to allocate state-funded urban system funding are not equally weighted.

Performance is weighted 50 percent for urban systems compared to 35 percent for rural systems in calculating funding.

This research effort was expanded to incorporate an analysis of the state-funded urban providers as a result of the interest expressed by these operators.

4

ORGANIZATION OF THE RESEARCH REPORT

This research report is organized as follows:

Chapter 2 contains a review of how other states use transit peer grouping and performance measurement.

Chapter 3 provides a summary discussion of the clustering analysis technique used to identify peer groups and lists the recommended rural and urban peer groups.

Chapter 4 examines the performance within and between rural and urban peer groups. Chapter 5 contains case studies of agencies that excel in terms of operating efficiency

and/or effectiveness and identifies key attributes for success. Appendices A and B contain more detailed information regarding the clustering analysis for rural and urban peer groups, respectively. Appendix C presents the effectiveness and efficiency measures by transit district. The fact-finding questions for the case study research are included as Appendix D.

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CHAPTER 2: TRANSIT PEER GROUPING AND PERFORMANCE TRACKING BY OTHER STATES

Rural and small urban communities throughout the United States have a unique set of characteristics; these same communities have an equally unique set of public transportation service needs. With the demographic projections across the United States indicating that rural and small urban transit needs will continue to increase into the future, it is becoming more important to maximize service for every funding dollar. In so doing, the over-arching goal of this project is to explore performance benchmarks that lead to improving effectiveness and efficiency of rural and small urban transit throughout Texas as well as increase the return on federal and state rural and small urban transit investments. Chapter 2 provides an overview of the Federal Transit Administration (FTA) federal-aid grant programs designated for small urban and rural areas. This is followed by a discussion of an approach to measuring performance consistently in FTA Section 5307 and FTA Section 5311 programs. The final section presents the results of a national scan conducted on small urban and rural transit systems, which reflect the current state of the practice in terms of improving performance.

GOVERNMENTAL STANCE ON FUNDING SMALL URBAN AND RURAL TRANSIT

Transit, often considered a necessary public service, receives federal, state, and local governmental financial assistance. The transit assistance is explicitly identified in government budgets and appropriations, partly to cover a government-induced gap between expenses and revenues and simultaneously provide sufficient public transportation service to its ridership. In recognition of minimizing this gap as well as meeting a growing need for public transportation services, on August 10, 2005, President George W. Bush signed the SAFETEA-LU legislation. SAFETEA-LU provided $286.4 billion in guaranteed funding for federal surface transportation programs over a five-year period (FY 2005 through FY 2009) and included $52.6 billion for federal transit programs. The $52.6 billion was a 46 percent increase over transit funding guaranteed in the prior authorization, the Transportation Equity Act for the 21st Century (TEA-21). Moreover, FTA administers an array of transit programs as outlined in Chapter 53, Title 49 of the United States Code (USC). For the purpose of this report, two specific federal-aid-formula-funded transit programs, namely the Urbanized Area (Section 5307) and the Other than Urbanized Areas (Section 5311), commonly known as Rural, are used as the framework to characterize best practices to consider when measuring performance consistently.

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SECTION 5307 AND SECTION 5311 GRANT PROGRAMS

The Section 5307 program makes federal resources available to urbanized areas with populations of 50,000 or more (1). The Section 5311 program provides financial assistance to support public transportation services, which are open to the public on an equal basis, in areas outside an urbanized area. The specific goals of the Section 5311 program are (2):

To enhance the access of people in non-urbanized areas to health care, shopping, education, employment, public services, and recreation.

To assist in the maintenance, development, improvement, and use of public transportation systems in rural and small urban areas.

To encourage and facilitate the most efficient use of all federal funds used to provide passenger transportation in non-urbanized areas through the coordination of programs and services.

To assist in the development and support of intercity bus transportation. To provide for the participation of private transportation providers in non-urbanized

transportation to the maximum extent feasible. Based on the FTA funding authorization for transit systems operating in urban areas and rural areas during FY 2005–FY 2009, Table 2 indicates that the Section 5307 program received an average annual increase of $131.5 million, or about 3.65 percent increase per year. The Section 5311 program average annual increase was $53.5 million, or about 21.3 percent increase per year. SAFETEA-LU featured a significant increase in rural transit investment. Table 3 provides a brief overview of major components of each program.

Table 2. FTA Formula Grant Authorizations FY 2005–FY 2009. Basic Urbanized Formula (Section 5307)

2005 2006 2007 2008 2009 Total $3,593 M $3,432 M $3,570 M $3,872 M $4,119 M $18,586 M

Formula Grants for Other than Urbanized Areas—Rural (Section 5311)

2005 2006 2007 2008 2009 Total $251 M $388 M $404 M $438 M $465 M $1,946 M

Source: (1)

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Table 3. Federal-Aid Transit Programs (Formula Funded). Descriptive Components Section 5307 Section 5311 Apportionment Apportioned on the basis of legislative

formulas. For urbanized areas with 200,000 in population and over, funds are apportioned and flow directly to a designated recipient selected locally to apply for and receive federal funds. For urbanized areas under 200,000 in population, the funds are apportioned to the governor of each state for distribution. A few areas under 200,000 in population have been designated as transportation management areas and receive apportionments directly.

Under formula grants. Funding is apportioned by a statutory formula that is based on the latest U.S. Census figures to non-urbanized areas. 80% of the statutory formula is based on the states’ non-urbanized population. 20% of the formula is based on land area. No state may receive more than 5% of the amount apportioned for land area. FTA adds amounts apportioned based on non-urbanized population according to the growing states formula factors of Title 49, USC, Section 5340 to the amounts apportioned to the states under the Section 5311 program.

Eligible Purposes Provide capital assistance to transit systems in urbanized areas over 200,000 in population. Provide both capital assistance and operating assistance to transit systems in small urbanized areas with populations from 50,000 to 200,000.

Capital, operating, and administrative purposes.

Eligible Recipients Public bodies with the legal authority to receive and dispense federal funds. Governors, responsible local officials, and publicly owned operators of transit services are to designate a recipient to apply for, receive, and dispense funds for transportation management areas pursuant to Title 49, USC, Section 5307(a) (2). Generally, a transportation management area is an urbanized area with a population of 200,000 or over. The governor or governor’s designee is the designated recipient for urbanized areas between 50,000 and 200,000.

State and local governments, Indian tribes, private non-profit organizations, and public transit operators that provide general public transportation services. Private for-profit providers of service are eligible through purchase of service agreements with a local public body for the provision of public transportation services.

8

Table 3. Federal-Aid Transit Programs (Formula Funded) (Continued). Descriptive Components Section 5307 Section 5311 Eligible Activities Planning, engineering design, and

evaluation of transit projects and other technical transportation-related studies; capital investments in bus and bus-related activities such as replacement of buses, overhaul of buses, rebuilding of buses, crime prevention and security equipment, and construction of maintenance and passenger facilities; and capital investments in new and existing fixed guideway systems including rolling stock, overhaul and rebuilding of vehicles, track, signals, communications, and computer hardware and software. All preventive maintenance and some Americans with Disabilities Act (ADA) complementary paratransit service costs are considered capital costs. In these areas, at least 1% of the funding apportioned to each area must be used for transit enhancement activities such as historic preservation, landscaping, public art, pedestrian access, bicycle access, and enhanced access for persons with disabilities. For urbanized areas with populations less than 200,000, operating assistance is an eligible expense.

Capital, operating, and administrative assistance to state agencies, local public bodies, Indian tribes, nonprofit organizations, and operators of public transportation services.

Allocation of Funding For areas of 50,000–200,000 in population, the formula is based on population and population density. For areas with populations of 200,000 or more, the formula is based on a combination of bus revenue vehicle miles, bus passenger miles, fixed guideway revenue vehicle miles, and fixed guideway route miles as well as population and population density.

Planning, training, and related technical studies are currently funded entirely with federal funds. State administration, planning, and technical assistance activities are limited to 15% of the annual apportionment. States must spend 15% of the apportionment to support rural intercity bus service unless the governor certifies, after consultation with affected intercity bus providers, that the intercity bus needs of the state are adequately met.

Match The grant recipient must provide match for the non-federal share of any project. The federal share may not exceed 80% of the net project cost for capital projects and not more than 50% of the next project cost (the operating deficit) for operating assistance. The cost of vehicle-related equipment attributable to compliance with the ADA and the Clean Air Act may be eligible for 90% federal share. Projects or portions of projects related to bicycles may also be 90% federal share.

The maximum federal share for capital and project administration is 80% (except for projects to meet the requirement of ADA, Clean Air Act, or bicycle access projects, which may be funded at 90%). For operating assistance, the maximum federal share is 50% of the net operating costs. The local share is 50%, which shall come from an undistributed cash surplus, a replacement or depreciation cash fund or reserve, or new capital.

Funding Availability The year apportioned plus three years, for a total of four years.

Year apportioned plus two years, for a total of three years.

Data Collection Report data to the National Transit Database (NTD).

Report data on service levels, fleet, costs, and revenues to the NTD.

Source: (2)

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PERFORMANCE MEASUREMENT, PEER GROUPS, AND BENCHMARKING

Evaluation is a vital and important element of any successful business. Levinson reported that business managers monitor programs and conduct evaluations to determine whether goals and objectives are achieved and how well the business is functioning (3). Similarly, in the transit industry, evaluations enable system operations to monitor efficiency, to measure effectiveness, and to generate data that can be used to improve overall service delivery as discussed in the Oluwoye and Gooding pilot study findings (4). Fielding and Smerk and Gerty believe that good transit management practices require regular evaluations of performance (5, 6). Fielding argues that a transit manager who does not measure and monitor performance is merely supervising operations (5). Data from evaluations must be used to identify and remedy problems, to justify budgets and expenditures, to gauge improvements in performance, and to document the system’s impact on the community (6). Smerk and Gerty further recommend yearly internal evaluations on key functional areas (e.g., maintenance, finances, and staff performance) and three-year comprehensive evaluations on each aspect of transit management and operation (6). With regard to the achievement of transit goals and objectives outlined in Section 5307 and Section 5311 programs, evaluations will be essential to the rural and small urban transit system planning process. Regular evaluations for these programs will potentially provide the database to document performance, to provide transit managers with a yardstick or benchmark to improve or plan for future services, and to persuade funding agencies that more money is needed to improve service delivery or to justify the continuation of existing transit service (7).

Performance Measures

Traditionally, small urban and rural transit systems have based their performance measures on readily available data such as cost per mile or cost per trip (7). As passengers expect more and funding continues to tighten and diversify, performance measures are an important input in an agency’s decision-making process to improve productivity and quality of service.

Why Measure Performance?

In an organization as complex as a transit system, there is an enormous variety of statistics and myriad performance measures from which to choose. It is crucial to pick the measurements based on what the agency is trying to evaluate. For instance, the agency may need to measure performance to (7):

Evaluate a contract provider to ensure competitive performance. Decide what service mode is better for a new area. Reduce service but have many options as to where. Evaluate various expense categories as part of a budget-review process. Evaluate results from a previous service or operational change. Document the impact of service or its improvement as part of a funding arrangement. Convince decision makers that transit service is a vital part of the community.

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What to Measure?

According to Radow and Winters, there are generally four ways to measure performance (7). While these are not inclusive, they do outline a useful way of thinking about how a system performs and the different ways to capture its unique attributes. These four categories are as follows:

Effectiveness measures are those that weigh how much a service is used against how much service is provided (e.g., the number of trips per vehicle hour).

Efficiency measures are those that focus on how much service is provided as compared to the resources that service requires (e.g., the cost per trip or passengers per vehicle hour).

Quality measures focus on attributes such as speed, safety, reliability, and comfort. Impact measures are results oriented: How is the service affecting the community and

region? How much of the population is being served? What share of needs is being met? How does the service increase income or reduce other costs? Nontraditional measures are most likely to be impact measures.

Recognizing the Differences between Urban and Rural Transit Systems

Rural transportation providers face unique challenges. Those identified by Radow and Winters are listed below (7):

Operating in large geographic areas with low population densities. Providing service to rural residents with lower incomes, generally, than those of urban

residents. Operating demand-response or subscription services. Providing transportation service largely to transit-dependent groups (e.g., the elderly,

youths, people with low income, or people with disabilities). Despite these facts, performance measurements used by small urban and rural transit systems are in many agencies the same as those used by major urban systems. Decision makers must be made aware that there are profound differences between small urban, rural, and urban transit. Once the differences between transit systems are made clear, operators must have some performance measures to fill the gap between what is expected and what gives a more accurate picture of the system (7).

Information: Where to Get It

The data used as a basis for identifying performance measures must be consistent. Data should cover a full year of operations since performance can vary greatly from season to season or even month to month. Data that vary widely can inspire suspicion in decision makers. Gathering data can be a problem for many small systems. For transit agencies where the staff often performs many functions simultaneously, a systematic approach to data collection is important. Accurate record keeping and an organized, integrated database may be one of the transit system’s most important analytical tools. Poor data collection techniques can lead to unreliable statistics, misleading performance measures, and poor decisions. For a comprehensive review of performance measures, the details of obtaining them, and pros and cons of different measures,

11

researchers recommend reviewing Transit Cooperative Research Program (TCRP) Report 6, Users’ Manual for Assessing Service-Delivery for Rural Passenger Transportation (8).

Benchmarking

Before a transit system can evaluate its performance, it needs a benchmark against which it can compare its performance. One type of benchmark is the performance of similar systems in the state or region. The concept of benchmarking was adopted by many private organizations during the mid- to late-1980s as a standard business practice. In business, benchmarking is the process of identifying successful business practices, typically identified through performance measurement, and applying those concepts to another business in order to achieve the same successful results. In addition, a benchmark is more likely to be based on a system’s goals and objectives that have in turn been developed based on past performance. Benchmarking has been used with the public transportation industry since the early 1990s. In March 2008, TCRP published a report entitled Guidebook for Measuring, Assessing, and Improving Performance of Demand-Response Transportation (9). This report examined methodologies for assessing services, including conducting trend analysis within an agency, comparing performance to absolute norms or standards, and comparing performance to peer agencies. The guidebook recommends using multiple methodologies in order to assess performance from a variety of aspects. Listed below are three empirical examples of benchmarking used in the transit community. The Florida Department of Transportation, through the National Center for Transit Research, published a proposed methodology for benchmarking U.S. transit systems in 2004 (10). This study stopped short of providing performance benchmarks, but rather was designed to identify ―best in class‖ agencies as the first step of an overall benchmarking program. Transit agencies were divided into peer groups based on geography and system size only. Since this effort looked nationally, there was not a full understanding (or verification) of the performance data being employed to score individual agencies. While an interesting exercise, the effort does not appear to have been carried forward. The State of North Carolina is beginning a process of requiring the incorporation of performance measurement and benchmarking among the state’s transit providers (11). (North Carolina is one of a handful of states that allocates a portion of transit funds among providers using performance indicators.) The intent of the program is that each transit agency be able to track their performance trend line, compare their performance with the performance of a reasonable peer group, and have a reward/disincentive related to pre-established absolute values of certain indicators. Much of their work, to date, is similar to the Texas Department of Transportation (TxDOT) approach, which focuses on the dispersion of indicator values in identifying agencies with performance at both the high and low ends of the spectrum.

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London (Canada) Transit annually provides their governing body a report on their performance that includes a benchmarking element (12). For the fixed-route operation, they have identified a discrete peer group of eight other urban systems with similar populations. Factors that they compare include the following:

Service hours per capita. Passenger trips per capita. Passenger trips per revenue hour and per revenue kilometer. Direct operating cost per revenue hour. Revenue recovery.

Additionally, London (Canada) Transit compares their specialized services (paratransit) to the averages of all such operations across Canada, and factors similar to the aforementioned are compared.

Peer Grouping

The most difficult step in benchmarking is the establishment of the appropriate peer group. Peer groups are groups of systems that are considered sufficiently similar in circumstances so they can be fairly compared. In 2008, Hendren and Niemeier highlighted a few efforts undertaken to create peer groups for transportation policy and research (13). Fielding (5) clustered 311 transit agencies into five peer groups using size (number of vehicles), peak-to-base operating ratio, and average operation speed to highlight differences in transit agencies and to compare performance between peer groups. A more recent study used 16 variables to distinguish major features characterizing individual states and to develop peer groups (14). The variables used in this cluster analysis included state population, number of drivers, infrastructure miles, vehicle miles traveled, number of bridges, bond retirement, six resource measures, and four fuel tax measures. The limitations of the study were that it relied on one year of data captured in 1993, a relatively small set of variables, a number of controversial resource measures, and no growth variables. Nevertheless, the analysis provided an important platform from which the development of peer groups could be considered. The selection of an appropriate peer group is driven by the factors being compared. The mode (fixed route and paratransit) of service involved is clearly one of these. Data from a national sample of agencies funded under Section 5311 show that average costs per vehicle are substantially higher for agencies providing primarily fixed-route service than for agencies providing principally demand-response service. By the same token, the average number of trips per vehicle operated is a good deal higher for fixed-route service than for demand-response service (7).

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A measure that combines these two is the average cost per passenger trip. For agencies operating more than five vehicles, this average trip cost is substantially less for fixed-route service than for demand-response service. However, for smaller Section 5311 agencies, this is not necessarily true. Where the service area is limited to a town or city (and thus relatively densely populated), fixed-route service is less costly on average. However, where the service area is county wide (or multi-county) and more sparsely settled, demand response appears to be cheaper on average (7). In addition to the mode of service involved, peer group comparability can also be involved, such as the following factors determined by the literature review conducted as part of the TCRP research (15):

Size of the transit agency. Characteristics of the transit workforce. Whether or not administration and overhead are shared with another agency, thus

impacting comparable costs (7). Target ridership markets (e.g., general public or seniors and people with disabilities). Service area characteristics and operating environment (proximity to urban area). Type of routing and scheduling used by the transit agency. Type of organization operating the transit service (dedicated public transit provider,

agency, or private operator). Use of vehicles dedicated to transit or non-dedicated vehicles. requirement for advance reservation versus immediate service request; Use of advanced technology. Door-to-door or curb-to-curb service. Use of volunteers. Whether or not the transit agency provides Medicaid non-emergency transportation.

It is important to determine a basis for selecting common criteria to use when creating a peer group. The criteria could be, for example, those factors that will influence the efficiency and effectiveness of a system.

EMPIRICAL STATE OF THE PRACTICE: SECTION 5307 AND SECTION 5311 GRANT PROGRAMS

To gain a more empirical insight, in 2009, researchers conducted a national survey of public transportation agencies that are responsible for managing Section 5307 and Section 5311 transit systems. Researchers pursued answers to the following four research questions:

Do states use peer grouping? If so, what is the state’s process for developing peer groups? For what reason(s) do states use peer grouping? Do states use performance measures/factors to allocate transit funds?

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Table 4 shows how peer grouping and performance measures are used among state departments of transportation (DOTs). Table 5 shows the breakout of states that conduct peer grouping by how the state uses peer groups. Eight states (Idaho, Indiana, Louisiana, Michigan, Minnesota, Mississippi, Nebraska, and North Carolina) indicated using peer grouping. Of the eight states, 25 percent of the states (Indiana and North Carolina) used peer grouping to assist with funding decisions and performance improvements.

Table 4. Peer Grouping and Performance Measures. Survey Categories Number of States Conduct Peer Grouping 8 -Use in Coordination Plans 1 -Use for Evaluations 3 -Use for Funding Decisions 3 -Use for Performance Improvement 3 Use Performance Measures 19 -Use in Annual Application Competitive Process

7

-Use for Evaluations 4 -Use in Funding Allocation 8

Table 5. Use of Peer Groups.

State Coordination Plans Evaluations Funding Decisions

Performance Improvements

Idaho x Indiana x x Louisiana x Michigan x Minnesota x Mississippi x Nebraska x North Carolina x x Totals 1 3 3 3

Table 6 lists how states use performance measures. Performance measures alone are used more broadly than peer groups combined with performance measures. Only eight states reported using performance indicators in allocating transit funds. Further, the degree to which these measures are used in funding allocation varies broadly. For example, Indiana and North Carolina use peer grouping and performance measures as the sole basis for fund allocation. Texas uses performance to determine 50 percent of state funding for qualified urban systems and 35 percent of federal and state funding for rural transit districts.

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Table 6. Usage of Performance Measures.

State Annual Application Competitive Process Evaluations

Funding Allocations

California x Idaho x Indiana x Iowa x Louisiana x Maryland x Massachusetts x Michigan x Minnesota x Mississippi x New Mexico x North Carolina x Ohio x Pennsylvania x South Dakota x Texas x West Virginia x Wisconsin x Wyoming x Totals 7 4 8

The performance measures used varied from the standard basic efficiency and effectiveness indicators to as few as one indicator, ridership. Table 7 provides a composite snapshot of eight states (Idaho, Indiana, Louisiana, Michigan, Minnesota, Mississippi, Nebraska, and North Carolina) responding to using peer grouping. The two states that resonate from among the eight are Indiana and North Carolina. Both of these states have profiles that are directly aligned with the research questions pursued in the survey, as well as the potential to be considered a best practice leading to improving small urban and rural transit efficiency and effectiveness.

Table 7. Composite Peer Grouping and Performance Measures State of the Practice. State

Peer Grouping Usage Performance Measures Usage

Coordinated Plans Evaluations

Funding Decisions

Performance Improvements

Annual Application Competitive

Process Evaluations Funding

Allocations Idaho x x Indiana x x x Louisiana x x Michigan x x Minnesota x x Mississippi x x Nebraska x North Carolina x x x Totals 1 3 3 3 1 2 4

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CHAPTER 3: CLUSTERING ANALYSIS

Chapter 3 documents the clustering analysis process used to establish peer groups. The first section of Chapter 3 describes the process and results of selection of rural peer groups. The second section of Chapter 3 shows the results of the application of the same process for the group of state-funded urban agencies. Appendices A and B contain detailed data from the multiple iterative calculations conducted as part of the clustering analysis process.

INTRODUCTION TO THE RURAL CLUSTERING ANALYSIS PROCESS

The purpose of the clustering analysis is to establish reasonable peer groupings of rural transit agencies. These peer groupings are to be based on the transit environment within which each agency operates so that agencies can be compared to other rural operators who face similar environments. The 37 rural transit agencies are expected to be divided into five to eight peer groups before the performance comparison is carried out within those groups. South Padre Island, the 38th rural provider, is excluded from this analysis. The small service area size, use of fixed-route shuttle service, and seasonality of a significant non-resident population are unique from all other rural providers. All the data used in this analysis reflect each provider service area’s inherent characteristics, which transit agencies cannot modify.. The following variables used in this analysis are representative of the kinds of data used in other research efforts to define the degree to which development and demographics are conducive to use of transit.

Population. Service area size. Service area density. Percent of service area population that is age 65 or older. Percent of households (HHs) with zero automobiles. Percent of population below poverty level. Percent of population age 21 to 64 that are disabled. Service area located in a border area. Service area located within/adjacent to a metropolitan area having a dedicated transit

sales tax. There are two category variables (0/1) that cannot be used in the clustering analysis directly. One is the variable representing whether the area is on the border, and the other represents whether the area is within a metropolitan area.

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Therefore, researchers investigated three options in the analysis:

The first option (C1) is to divide all the agencies into four categories according to those two 0/1 variables and then perform the clustering.

The second option (C2) is to treat those two 0/1 variables just as other continuous variables and input them into the clustering analysis.

The last option (C3) is to ignore them in the clustering analysis and see whether there is some relationship between the geographic locations with the clustering results.

The variables are not totally independent from each other. Figure 2 and Table 8 contain plotted correlations between all the continuous variables. The scatter matrix and the correlation coefficient show that the percentage of the people under the poverty level is highly correlated to households without autos. The report contains insight about the correlation between the category variables and some of the continuous variables in a later section.

Figure 2. Scatter Matrix of Variables.

Population

Land Area

Seniors

HH without

Autos

Poverty

Disabled

Density

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Table 8. Correlation Coefficients between Variables.

Pop. Land Area

% Pop. 65+

% HHs with Zero Autos

% Individuals

under Poverty

Level % Disabled Ages 21–64 Density

Population 1.0000 0.3668 0.2531 −0.1477 −0.1506 0.0066 0.0882 Land Area 0.3668 1.0000 0.2703 −0.0452 0.0747 0.1116 −0.4449 % Pop. 65+ 0.2531 0.2703 1.0000 −0.2666 −0.2132 −0.0232 −0.3511 % HHs with Zero Autos

−0.1477 −0.0452 −0.2666 1.0000 0.7584 0.5447 −0.3565

% Individuals under Poverty Level

−0.1506 0.0747 −0.2132 0.7584 1.0000 0.5255 −0.4754

% Disabled Ages 21–64

0.0066 0.1116 −0.0232 0.5447 0.5255 1.0000 −0.1830

Density 0.0882 −0.4449 −0.3511 −0.3565 −0.4754 −0.1830 1.0000

CLUSTERING ANALYSIS PROCESS

Since the nature of the variables is different, the magnitudes vary greatly. In calculations, the ones with large magnitude would totally overwhelm those with small magnitude. To avoid that, all the variables input into the clustering analysis need to be normalized before the clustering analysis. In this transformation, the measure then represents how far the variable values deviate from the mean.

iijij

i

x Xy

s where: yij is the adjusted ith variable in jth case, xij is the ith variable in j th case,

iX is the mean of the ith variable, and si is the standard deviation of the i th variable. In addition, considering the importance of the social-economic elements’ influence on prospective utilization of a transit agency’s service, researchers assigned higher weights on two variables— Percentage of Households without Autos‖ and ―Percentage of Individuals under Poverty Level.‖ Therefore, the values of those two variables are doubled in the following clustering analysis. For every set of clustering results, researchers listed the component agencies of every cluster, the distance of each case (agency) to the center of the cluster (within-cluster distance), and the distance between the centers of different clusters (between-clusters distance). The first distance is used to measure the similarity within the peer group, while the latter one measures the differences between the clusters. Those agencies far away from their center are more likely to be regrouped when the number of clusters changes. Researchers also calculated the mean and standard deviation of each variable for every peer group as a measurement of that group’s characteristics.

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Researchers ran analyses for each option—C1, C2, and C3—using varying numbers of clusters. The solution that minimizes the variability within each group and maximizes the variability between groups is defined as the optimal solution. In C1, all the agencies are first partitioned into four categories according to their geographic locations. Then the K-mean clustering, as described above, is applied to each category to divide those agencies into two clusters. There is only one agency (El Paso County) in the 1/1 category, which means that it is near a metro area and on the border. Therefore, there are a total of seven, not eight, clusters. This methodology does not provide for more groups because of the initial step of pre-dividing agencies into four sub-sets. For C2 and C3, the number of clusters is set to be five, six, seven, or eight according to the intent established at the beginning of the process. To determine the optimum number of clusters, the sum of the F values in the analysis of variance (ANOVA) is used as the criterion. The formula of an F-test for each variable is:

A large F value is an indication of better peer grouping. A large value indicates a high variability between groups, with a low variability within a group.

ANALYSIS OF RESULTS

Each of the options treats the geographic indicators differently. C1 applies the two geographic indicator variables before applying the clustering analysis algorithm. It excludes the influence of geographic location in the clustering analysis itself. C1 compares the similarities and differences of agencies having similar locations instead of among all the study agencies. It excludes the possibility that the areas with different locations may share more similar characteristics. C1 essentially assigned those two variables very high weights. C2 uses the two geographic category variables just like other continuous variables in the clustering analysis. C2 assigns the geographic variables the same weight as the majority of other variables. The differences between C1 and C2 results are limited to those peer groups containing fewer cases and reflect those cases whose distance to the center of their cluster is relatively larger than their peers. For example, Kilgore is partitioned into a group alone in C1 because its population is significantly higher than that of other areas in the 0/0 category. However, in C2, it is grouped with San Antonio, Austin, and Bryan whose populations are high, too. The comparison means that for most agencies C1 and C2 result in the same clusters. However, C2 appears to provide a greater opportunity for equitable inclusion of all factors. C3 ignores both geographic variables. In order to assess the impact of ignoring those variables, researchers identified two continuous variables that might act as surrogates for the non-continuous variables. Based on empirical experience, researchers selected ―Density‖ to represent ―Metro Area‖ and ―Percentage of Households without Autos‖ for ―Border‖ to examine the strength of the correlation between each set of variables.

iability group within iability group between F

var var

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From the plots in Figure 3, the difference of the density in metro areas and non-metro areas is not consistent, with significant overlaps in densities between areas near metropolitan areas and those that are not near metropolitan areas. C3 would not correctly consider the potential impacts of being located near or far from an urban center.

Figure 3. Density versus Metro Region.

A second potential issue in the clustering results is the one-case clusters. The objective of clustering is to group all the agencies with their peers to permit comparison of performance indicators. If there is only one agency in a group, there is no opportunity for such a peer comparison. To attempt to address this issue, researchers performed the C2 clustering, ignoring the percentage of households without autos or population below poverty level for the five to eight cluster cases. Table 9 displays the results. For this alternative set of results, eight is the optimum number of clusters. However, there are still one-case clusters. Therefore, this alternative is not an improvement. Appendix A provides detailed data associated with all tested options. C1 and C2 provide two kinds of insight into the characteristics of the study areas. If there is need or requirement to emphasize the role the geographic location plays on the performance, C1 is recommended for the clustering analysis. If location information is of interest but no more important than other considerations, C2 is more appropriate since it assigns the equitable weighting to every variable. C3 is not recommended since it ignores some information.

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Table 9. ANOVA for C2 without HHs without Autos or Population below Poverty Level.

a. Five Clusters Cluster Error Variables Mean Square df Mean Square df F Population 6.527 4 0.330 33 19.779 Land Area 6.910 4 0.284 33 24.361 Density 5.042 4 0.510 33 9.886 % Pop. 65+ 6.065 4 0.386 33 15.710 % Disabled Ages 21–64 4.748 4 0.546 33 8.702 Border 8.577 4 0.082 33 105.079 Metro Region 1.245 4 0.970 33 1.283 Total 187.102

b. Six Clusters

Cluster Error Variables Mean Square df Mean Square df F Population 6.005 5 0.218 32 27.559 Land Area 5.742 5 0.259 32 22.160 Density 4.700 5 0.422 32 11.140 % Pop. 65+ 1.891 5 0.861 32 2.197 % Disabled Ages 21–64 5.541 5 0.290 32 19.079 Border 6.861 5 0.084 32 81.516 Metro Region 1.321 5 0.950 32 1.391 Total 202.662

c. Seven Clusters

Cluster Error Variables Mean Square df Mean Square df F Population 4.965 6 0.233 31 21.344 Land Area 4.777 6 0.269 31 17.765 Density 4.011 6 0.417 31 9.615 % Pop. 65+ 3.449 6 0.526 31 6.558 % Disabled Ages 21–64 4.728 6 0.279 31 16.972 Border 6.167 6 0 31 ∞ Metro Region 5.148 6 0.197 31 26.105 Total 135.565

d. Eight Clusters

Cluster Error Variables Mean Square df Mean Square df F Population 4.314 7 0.227 30 19.015 Land Area 4.808 7 0.111 30 43.145 Density 3.95 7 0.312 30 12.67 % Pop. 65+ 3.005 7 0.532 30 5.646 % Disabled Ages 21–64 4.078 7 0.282 30 14.469 Border 5.286 7 0 30 ∞ Metro Region 4.412 7 0.204 30 21.654 Total 147.569

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The recommended result is C2 with six clusters as the final clustering. For the one-case cluster in C2, researchers recommend merging that one case to a nearby cluster for the performance measurement comparison. Table 10 shows the recommended final clustering.

Table 10. Final Rural Cluster Results/Peer Groups.

C2 (Six Clusters) Group Rural Agency

1

Del Rio (Del Rio) Kleberg County Human Services (Kingsville) Lower Rio Grande Valley Develop. Council (McAllen) Rural Economic Assistance League, Inc. (REAL) (Alice)

2 West Texas Opportunities, Inc. (Lamesa)*

3

Ark-Tex Council of Governments (Texarkana) Aspermont Small Business Development Center (Aspermont) Bee Community Action Agency (Beeville) Caprock Community Action Association (Crosbyton) Central Texas Rural Transit District (Coleman) Colorado Valley Transit (Columbus) Concho Valley Council of Governments (San Angelo) Golden Crescent Regional Planning Commission (Victoria) Heart of Texas Council of Governments (Waco) Hill Country Transit District (San Saba) Panhandle Community Services (Amarillo) Rolling Plains Management Corp. (Crowell) South East Texas Regional Planning Commission (Beaumont) South Plains Community Action Association (Levelland)

4

Cleburne (Cleburne) Collin County Area Regional Transportation (McKinney) Community Services, Inc. (Corsicana) Fort Bend County Greenville Senior Center Resources and Public Transit Gulf Coast Center (Galveston) Kaufman County Senior Citizens Service (Terrell) Public Transit Services (Mineral Wells) Services Program for Aging Needs (SPAN) (Denton) Texoma Area Paratransit System (TAPS) (Sherman) The Transit System Inc. (Glen Rose)

5

Alamo Area Council of Governments (San Antonio) Brazos Transit District (Bryan/College Station) Capital Area Rural Transportation System (Austin) East Texas Council of Governments (Kilgore)

6

Community Action Council of South Texas (Rio Grande City) Community Council of Southwest Texas (Uvalde) El Paso County Webb County Community Action Agency (Laredo)

* Singleton cluster merged with cluster 5 for further analyses.

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URBAN SYSTEM CLUSTERING ANALYSIS

Researchers applied the clustering selection process, described above for the rural systems, to the state-funded urban systems. Having thoroughly examined the implications and impacts of the three options for consideration of locational parameters (C1, C2, and C3 above) when developing the rural cluster, urban cluster alternatives applied the selected C2 option only. C2 considered the locational data as a variable rather than as an initial screener (C1) or as data to be ignored (C3). The total number of urban agencies included in the clustering analysis was 27. The total number of urban transit agencies that are eligible for state funds is 30. Researchers identified the four urban transit agencies that are limited eligibility providers (Arlington, Grand Prairie, Mesquite, and Northeast Transportation Services [NETS]) as a peer group and did not include these agencies in further clustering analysis. One transit agency that is not a recipient of state funds was included in the clustering analysis, thus resulting in 27 agencies (Denton County Transportation Authority was included because the service area is similar to other state-funded transit districts). The number of alternatives tested ranges from four clusters to seven clusters. Table 11 displays the results of the ANOVA test for each alternative. Appendix B contains detailed data associated with all options.

Table 11. ANOVA Tests of Urban Cluster Alternatives.

a. Four Clusters (7/1/4/15)

Variables Cluster Error

F Mean Square df Mean Square Df

Population 6.138 3 0.373 23 16.446 Land Area (Square Miles) 6.656 3 0.306 23 21.775

Population/Square Mile 3.553 3 0.711 23 5 % Pop. Ages 21–64 Disabled 6.505 3 0.325 23 19.99

% Occupied Housing Units with Zero Autos 2.321 3 0.871 23 2.665

% Pop. Age 65+ 6.012 3 0.39 23 15.424 % Pop. below Poverty Level 2.385 3 0.863 23 2.764

% Management, Professional, and Related Occupations

5.485 3 0.458 23 11.965

% Service Occupations 3.202 3 0.756 23 4.234 % Production, Transportation, and Material Moving Occupations

4.193 3 0.627 23 6.686

Border Area 4.472 3 0.591 23 7.572 Metro Area 2.558 3 0.84 23 3.044 Total 117.565

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Table 11. ANOVA Tests of Urban Cluster Alternatives (Continued).

b. Five Clusters (8/12/2/4/1)


F Mean Square df Mean Square Df


Population/Square Mile 2.506 4 0.772 22 3.249 % Pop. Ages 21–64 Disabled 4.552 4 0.4 22 11.394




4.396 4 0.428 22 10.274


3.688 4 0.557 22 6.623


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c. Six Clusters (1/15/3/2/4/2)


F Mean Square df Mean Square df






3.374 5 0.482 21 6.995


3.027 5 0.565 21 5.359


27


d. Seven Clusters (3/6/2/12/1/2/1)


F Mean Square df Mean Square df






3.295 6 0.361 20 9.116


3.008 6 0.448 20 6.719


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The alternative with this largest total score is the five-cluster alternative. This alternative does result in one cluster with a single agency. As with the rural groups, the final urban groups merged the singleton with the nearest cluster, resulting in a net of four clusters. Table 12 displays the selected alternative. Researchers identified the four urban transit agencies that are limited eligibility providers (Arlington, Grand Prairie, Mesquite, and NETS) as an additional urban peer group.

Table 12. Final Urban Cluster Results/Peer Groups. Cluster No. Name

1

Beaumont Municipal Transit City of Port Arthur Hill Country Transit District, Temple Division Longview Transit Texarkana Urban Transit District Texoma Area Paratransit System, Inc., Sherman/Denison Tyler Transit Waco Transit System

2

Brazos Transit District, Bryan/College Station City of Abilene, Texas City of Amarillo, Amarillo City Transit City Transit Management Company, Inc., Lubbock Concho Valley Transit District Denton County Transportation Authority* Golden Crescent Regional Planning Commission, Victoria Gulf Coast Center/Connect Transit, Lake Jackson/Angleton Gulf Coast Center/Connect Transit, Texas City/La Marque Hill Country Transit District, Killeen Division Midland-Odessa Urban Transit District Wichita Falls Transit System

3 Brazos Transit District, The Woodlands Collin County Area Regional Transportation

4

City of Brownsville City of Galveston City of Harlingen Laredo Transit Management Incorporated

5 Hidalgo County combined (McAllen)** * The Denton County Transportation Authority does not receive state funds and does not appear in this report after the clustering analysis is discussed. ** Combined with cluster 4 to eliminate single-agency cluster.

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RURAL AND URBAN PEER GROUPING SUMMARY

Researchers conducted the cluster analysis to develop peer groupings for rural and urban transit districts separately. Rural and urban transit districts in Texas differ in service area and delivery characteristics. Researchers recognize the importance of differentiating between rural and urban transit systems. Rural transit districts differ from urban transit districts because rural transit districts typically:

Operate in large geographic areas with low population densities. Operate demand-response services versus fixed-route services.

The cluster analysis provides peer groupings based on the transit environment so that transit districts can be compared to other rural or urban transit districts that face similar environments. Table 13 and Table 14 provide a summary of the rural and urban peer groupings and the environmental factors associated with each transit district.

Table 13. Rural Peer Groupings and Environmental Data Elements.

Transit District

Population Density

(Population/Square Mile)

% Population

with a Disability

(Ages 21–64)

% Occupied Housing

Units with Zero Autos

% Population

Age 65+

% Population

below Poverty

Level

Border, Major Metro,

or Both* Rural Peer Group 1:

Del Rio 14.15 5.2 8.3 10.8 25.7 Border Kleberg County Human Serv. 13.73 19.9 12.3 10.9 25.3 Metro Lower Rio Grande Valley Dev. Council 46.44 21.8 10.6 11.9 26.8 Border Rural Economic Asst. League 38.91 26.0 9.9 12.0 23.3 Mean 28.31 18.2 10.3 11.4 25.3

Rural Peer Group 2:

Ark-Tex Council of Governments 38.48 24.1 7.9 15.8 15.7 Aspermont Small Bus. Dev. Center 6.31 21.5 6.1 18.6 15.2 Bee Community Action Agency 18.72 24.6 7.5 14.9 18.0 Caprock Community Action Agency 9.82 21.9 7.0 14.1 18.7 Central Texas Rural Transit District 17.29 22.4 6.1 17.0 15.6 Colorado Valley Transit 36.37 20.1 8.6 13.7 14.9 Concho Valley Transit District 3.69 19.9 5.6 16.0 15.6 Golden Crescent Regional Planning Council 22.62 22.4 8.3 15.6 15.7 Heart of Texas Council of Governments 30.73 23.0 6.5 16.1 12.9 Hill Country Transit District 18.67 20.7 5.6 17.4 19.2 Panhandle Community Services 8.68 19.3 5.1 13.8 13.6 Rolling Plains Mgmt. Corporation 13.14 22.1 5.8 17.4 12.8 South East Texas Regional Planning Commission 64.69 21.1 6.9 12.4 10.6 South Plains Community Act. Agency 15.11 21.4 5.9 13.8 16.6 Mean 22.50 21.7 6.7 15.5 15.4

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Table 13. Rural Peer Groupings and Environmental Data Elements (Continued).

Transit District

Population Density

(Population/ Square Mile)

% Population

with a Disability

(Ages 21–64)

% Occupied Housing

Units with Zero Autos

% Population

Age 65+

% Population

below Poverty Level

Border, Major Metro,

or Both* Rural Peer Group 3:

Cleburne 145.41 21.7 4.9 10.3 9.0 Metro Collin County Area Regional Transp. 82.03 17.3 3.8 7.7 1.9 Metro Community Services, Inc. 70.38 22.7 6.9 11.9 12.3 Metro Fort Bend County 50.72 17.2 3.5 6.5 2.3 Metro Gulf Coast Center 65.43 22.2 11.4 11.9 3.4 Metro Kaufman Area Rural Transportation 92.34 21.2 5.4 10.5 10.2 Metro Public Transit Services 42.51 20.1 5.1 12.7 9.4 Metro Senior Center Res. and Public Transit 91.08 23.5 6.4 12.7 12.4 Services Program for Aging Needs 83.49 15.5 2.8 7.5 6.0 Metro Texoma Area Paratransit System 35.83 20.3 5.0 14.5 10.7 The Transit System, Inc. 78.67 18.8 2.8 17.2 8.4 Mean 76.17 20.1 5.3 11.2 7.8

Rural Peer Group 4:

Alamo Area Council of Governments 38.80 21.0 5.6 14.8 12.7 Metro Brazos Transit District 47.20 22.9 7.5 13.3 14.5 Metro Capital Area Rural Transp. System 59.49 17.0 4.8 12.2 10.0 Metro East Texas Council of Governments 58.84 24.3 6.7 15.7 13.8 West Texas Opportunities 4.33 23.5 7.4 12.9 18.4 Mean 41.73 21.7 6.4 13.8 13.9

Rural Peer Group 5:

Community Action Council South Texas 16.35 32.2 13.6 10.4 42.9 Border Community Council of Southwest Texas 9.83 47.9 11.8 12.2 31.4 Border El Paso County 38.51 28.4 11.4 6.6 37.3 Both Webb County Community Action Agency 5.29 28.4 14.4 5.6 45.8 Border Mean 17.50 34.2 12.8 8.7 39.3 Rural Summary:

Group 1 28.31 18.2 10.3 11.4 25.3 Group 2 22.50 21.7 6.7 15.5 15.4 Group 3 76.17 20.1 5.3 11.2 7.8 Group 4 41.73 21.7 6.4 13.8 13.9 Group 5 17.50 34.2 12.8 8.7 39.3 * Blank cells indicate the rural transit district is not adjacent to the Texas-Mexico border or a major metropolitan area.

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Table 14. Urban Peer Groupings and Environmental Data Elements.

Transit District

Population Density

(Population/ Square Mile)

% Population with a

Disability (Ages 21–64)

% Occupied Housing Units

with Zero Autos % Population

Age 65+

% Population

below Poverty

Level

Border or Major Metro*

Urban Peer Group 1: Beaumont 1,339.9 13.6 12.4 13.4 19.6

Longview 1,337.4 12.7 7.8 13.6 16.1 Port Arthur 696.7 14.1 15.8 15.8 25.2 Sherman 1,749.7 13.9 8.4 16.0 13.8 Temple 888.8 11.4 10.3 14.6 14.7 Texarkana 1,402.3 14.2 12.5 15.4 22.0 Tyler 1,701.0 13.5 9.4 15.1 16.8 Waco 1,828.1 13.3 10.2 13.3 23.0 Mean 1,368.0 13.3 10.9 14.7 18.9

Urban Peer Group 2: Abilene 2,244.5 11.3 6.7 12.3 15.6

Amarillo 1,930.2 11.8 6.7 12.7 14.5 Bryan 1,602.5 7.0 7.2 6.5 29.4 Killeen 2,163.4 12.2 5.7 5.0 11.8 Lake Jackson 2,178.5 11.2 6.7 9.2 12.2 Metro

Lubbock 1,738.3 11.6 7.2 11.1 18.4 Midland-Odessa 1,800.9 10.1 7.3 12.0 15.7 San Angelo 1,931.6 12.0 7.6 14.1 15.6 Texas City 1,646.4 13.0 7.4 12.4 14.0 Metro

Victoria 1,832.2 11.8 8.4 12.3 14.7 Wichita Falls 1,469.9 1.0 7.6 12.3 13.9 Mean 1,867.1 11.2 7.1 10.9 16.0

Urban Peer Group 3: McKinney 2,025.4 7.6 4.3 6.8 8.0 Metro The Woodlands 2,385.6 6.0 3.8 7.5 4.2 Metro Mean 2,205.5 6.8 4.1 7.2 6.1

Urban Peer Group 4: Brownsville 2,896.1 14.7 13.1 9.2 37.1 Border Galveston 1,237.8 13.1 17.8 13.4 22.3 Metro Harlingen 1,689.9 11.6 11.2 15.3 24.9 Border Laredo 2,252.3 13.0 12.3 7.9 29.6 Border McAllen 1,539.1 13.1 9.7 10.8 33.1 Border Mean 1,923.0 13.1 12.8 11.3 29.4

Urban Summary Group 1 1,368.0 13.3 10.9 14.7 18.9

Group 2 1,867.1 11.2 7.1 10.9 16.0 Group 3 2,205.5 6.8 4.1 7.2 6.1 Group 4 1,923.0 13.1 12.8 11.3 29.4 * Blank cells indicate the rural transit district is not adjacent to the Texas-Mexico border or a major metropolitan area.

32

Four transit providers in Texas are designated as ―limited eligibility providers‖—Arlington, Grand Prairie, Mesquite, and NETS. These transit providers restrict transit eligibility to people age 65 and over as reported by the U.S. Census and people ages 5 to 64 with a U.S. Census defined disability. The four limited eligibility providers in Table 15 are in a separate peer grouping, and performance is compared within the four providers.

Table 15. Urban Peer Group—Limited Eligibility Providers.

Limited Eligibility Providers 2000 Total Population

2000 Eligible

Population* % Eligible Population

Arlington 335,164 86,396 25.8 Grand Prairie 126,889 37,995 29.9 Mesquite 123,800 34,209 27.6 NETS 313,030 77,713 24.8 Total Limited Eligibility Providers 898,883 236,313 26.3 *People age 65 and over and people with a disability ages 5 to 64.

Figure 4 displays the transit environmental factor averages for rural peer groupings. Rural Peer Group 3 includes a majority of transit districts that are within or adjacent to a major metropolitan area and has a significantly higher population density than the other rural peer groupings. Rural Peer Group 5 is comprised of all border communities and has a significantly higher percent of persons with disabilities and persons below the poverty level than the other peer groupings. Rural Peer Group 5 also has the highest percent of occupied housing units without automobiles.

Figure 4. Rural Peer Grouping Transit Environmental Factors.

0% 5%

10% 15% 20% 25% 30% 35% 40%

Group 1 Group 2 Group 3 Group 4 Group 5

Rural Peer Grouping Transit Environmental Factors

% of Population with a Disability (Ages 21 - 65) % of Households with Zero Automobiles % of Population Age 65 and Older % of Population below Poverty Level

28 23

76

42

18

0

20

40

60

80

Group 1

Group 2

Group 3

Group 4

Group 5

Rural Peer Groupings Population Density

(Population per Square Mile)

33

Figure 5 displays the transit environmental factor average for urban peer groupings, excluding the peer grouping for limited eligibility providers. The peer group for limited eligibility providers is not included in this comparison because the environmental factors are different from all other urban peer groups. Urban Peer Group 3 consists of The Woodlands and McKinney and has the lowest transit environmental factors outside of population density. Urban Peer Group 4 has a significantly higher percent of persons below the poverty level than do the other urban peer groupings.

Figure 5. Urban Peer Grouping Transit Environmental Factors.

0%

5%

10%

15%

20%

25%

30%

35%

40%

Group 1 Group 2 Group 3 Group 4

Urban Peer Grouping Transit Environmental Factors

% of Population with a Disability (Ages 21 - 65) % of Households with Zero Automobiles % of Population Age 65 and Older % of Population below Poverty Level

1,368

1,867 2,205

1,923

-

500

1,000

1,500

2,000

2,500

Group 1

Group 2

Group 3

Group 4

Urban Peer Groupings Population Density

(Population per Square Mile)

35

CHAPTER 4: PERFORMANCE MEASURES

The purpose of this section is to calculate the effectiveness and efficiency performance measures for each peer group and provide a comparison across peer groups. This section includes a comparison of the peer group performance averages and a comparison of the effectiveness and efficiency performance measures for each transit district by peer group.

EFFECTIVENESS AND EFFICIENCY MEASURES BY PEER GROUP

Researchers calculated effectiveness and efficiency measures using fiscal year 2009 data for each transit district and calculated the mean (average) and median for each peer group. Effectiveness measures are those that weigh how much a service is used (passengers) against how much service or resources are required (miles, hours, or expenditure). Efficiency measures are those that focus on how much service is provided (miles or hours) as compared to the resources that service requires (expenditure). The South Padre Island transit district effectiveness and efficiency measures are not listed because this transit district is an outlier of the rural transit district performance. Because the South Padre Island transit district serves a tourist population and a highly dense service area of 1,424 population per square mile operating a fixed-route circulator, this service environment results in effectiveness measures atypical to rural transit districts.

Comparison of Average Operating Effectiveness and Efficiency by Peer Group

Table 16 displays the average effectiveness and efficiency performance measures for each peer group, and Figure 6 plots these data. For rural peer groups, the average performance measures are very similar; however, for urban peer groups, one peer group is unusual. Urban Peer Group 4 consisting of Laredo, Brownsville, and McAllen has significantly higher operating effectiveness performance but is offset by lower operating efficiency performance. Urban Peer Groups 3 and 4 have similar population densities but have very different transit environmental demographic factors. Urban Peer Group 4 has a significant percent of population below poverty level.

36

Table 16. Peer Group Comparison of Operating Effectiveness and Efficiency.

Transit District

Operating Efficiency

Revenue Miles per Operating Expense

Operating Effectiveness

Passenger Trips per Revenue Mile

Rural Peer Groups Peer Group

Average Peer Group

Average Peer Group 1 (R1) 0.36 0.24 Peer Group 2 (R2) 0.40 0.17 Peer Group 3 (R3) 0.42 0.14 Peer Group 4 (R4) 0.37 0.15 Peer Group 5 (R5) 0.34 0.29 Urban Peer Groups

Peer Group 1 (U1) 0.28 0.52 Peer Group 2 (U2) 0.30 0.61 Peer Group 3 (U3) 0.31 0.60 Peer Group 4 (U4) 0.18 1.20 Limited Eligibility (Limited) 0.33 0.17

Figure 6. Peer Group Average Operating Effectiveness and Efficiency.

U1

U2

U3

U4

Limited

R1

R2 R3R4

R5

-

0.20

0.40

0.60

0.80

1.00

1.20

1.40

0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45

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Revenue Miles per Operating Expense Efficiency

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Rural Transit District Effectiveness and Efficiency Measures by Peer Group

Rural Peer Group 1

The first rural peer group is comprised of four rural transit districts (Table 17). Table 17 is sorted from the lowest operating expense per passenger trip of $7.11 to the highest of $17.58. One transit district in Peer Group 1, Rural Economic Assistance League, has the lowest operating expense per passenger trip and performs above the peer group average in both operational effectiveness and operational efficiency. Figure 7 illustrates those transit districts in Peer Group 1 that perform above the peer average in operating effectiveness and/or operating efficiency measures. Peer Group 1 rural transit districts with high performance in operating effectiveness (above the peer group average) are:

Rural Economic Assistance League. Del Rio. Kleberg County Human Services.

Rural Economic Assistance League and the Lower Rio Grande Valley Development Council are the two Peer Group 1 rural transit districts with higher performance (above the peer average) for operating efficiency.

Table 17. Rural Peer Group 1—Effectivness and Efficiency Measures.

Transit District Code

Operating Efficiency Revenue Miles per Operating Expense

Cost Effectiveness

Operating Expense per Passenger

Trip


Passenger Trips per

Revenue Mile Rural Economic Assistance League REAL 0.46 $7.11 0.31 Del Rio DR 0.33 $11.95 0.25 Kleberg County Human Services KCHS 0.24 $16.25 0.26 Lower Rio Grande Valley Develop. Council LRGVDC 0.41 $17.58 0.14 Peer Group Average 0.36 $13.22 0.24

38

Figure 7. Rural Peer Group 1—Effectiveness and Efficiency Measures.

Rural Peer Group 2

Rural Peer Group 2 is comprised of 14 rural transit districts (see Table 18). Table 18 is sorted by the lowest operating expense per passenger trip of $6.01 to the highest of $54.24. Two transit districts in Peer Group 2, the Ark-Tex Council of Governments and Rolling Plains Management Corporation, perform above the peer group average for both operational effectiveness and operational efficiency. Figure 8 illustrates those transit districts in Peer Group 2 that perform above the peer average for operating effectiveness and/or operating efficiency measures. Peer Group 2 rural transit districts with higher performance (above the peer group average) for operating effectiveness are:

Ark-Tex Council of Governments. Panhandle Community Services. Concho Valley Transit District. Hill Country Transit District. Rolling Plains Management Corporation.

DRKCHS

LRGVDC

REAL

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.10 0.20 0.30 0.40 0.50 0.60

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Revenue Miles per Operating ExpenseEfficiency

Above Peer Group Average

39

Peer Group 2 rural transit districts with higher performance for operating efficiency are:

Ark-Tex Council of Governments. Heart of Texas Council of Governments. Golden Crescent Regional Planning Commission. Aspermont Small Business Development Center. Caprock Community Action Association.1 Rolling Plains Management Corporation. Central Texas Rural Transit District.

Table 18. Rural Peer Group 2—Effectiveness and Efficiency Measures.



Revenue Miles per Operating

Expense

Cost Effectiveness

Operating Expense per

Passenger Trip


Passenger Trips per Revenue

Mile Ark-Tex Council of Governments AKTXCOG 0.55 $6.01 0.30 Panhandle Community Services PCS 0.38 $8.76 0.30 Rolling Plains Management Corp. RPMC 0.43 $12.24 0.19 Hill Country Transit District HCTD 0.36 $14.08 0.20 Golden Crescent Regional Planning Comm. GCRPC 0.53 $14.21 0.13 Caprock Community Action Assoc. CCAA 0.43 $15.47 0.15 Colorado Valley Transit CVT 0.36 $18.53 0.15 Central Texas Rural Transit District CTRTD 0.42 $19.41 0.12 Heart of Texas Council of Governments HOTCOG 0.53 $20.63 0.09 Concho Valley Transit District CONCHO 0.20 $21.54 0.23 Bee Community Action Agency BCAA 0.38 $22.72 0.12 South Plains Community Action Assoc. SPCAA 0.33 $23.22 0.13 South East Texas Regional Planning Comm. SETRPC 0.23 $28.38 0.15 Aspermont Small Bus. Dev. Center ASBDC 0.45 $54.24 0.04 Peer Group Average

0.40 $19.96 0.17

1 Caprock became part of South Plains Community Action Association in 2010.

40


Rural Peer Group 3

Rural Peer Group 3 is comprised of 11 rural transit districts (see Table 19). South Padre Island is excluded from the peer group comparisons because the transit district is an outlier as compared to rural transit districts. South Padre Island is a tourist town providing a free-fare circulator fixed route that is atypical of a rural transit district. Table 19 is sorted by the lowest operating expense per passenger trip of $11.72 to the highest of $37.18. One transit district in Peer Group 3, Kaufman Area Rural Transportation, performs above the peer group average for both operational effectiveness and operational efficiency. Figure 9 illustrates those transit districts in Peer Group 3 that perform above the peer average for operating effectiveness and/or operating efficiency measures. Peer Group 3 rural transit districts with higher performance (above the peer group average) for operating effectiveness are:

Fort Bend County. Community Services, Inc. Kaufman Area Rural Transportation. Cleburne.

Peer Group 3 rural transit districts with higher performance for operating efficiency are:

Public Transit Services. Collin County Area Regional Transportation.

AKTXCOG

ASBDC

BCAA

CCAA

CTRTD

CVT

CONCHO

GCRPC

HOTCOG

HCTD

PCS

RPMC

SETRPC SPCAA

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.10 0.20 0.30 0.40 0.50 0.60

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41

Kaufman Area Rural Transportation. Senior Center Resources and Public Transit. Texoma Area Paratransit System.




Cost Effectiveness


Trip



Mile Fort Bend County FBC 0.40 $11.72 0.21 Community Services, Inc. CSI 0.41 $11.96 0.20 Kaufman Area Rural Transportation KART 0.48 $13.55 0.15 Public Transit Services PTS 0.63 $15.18 0.10 Collin County Area Regional Transportation CCART 0.60 $15.76 0.11 Senior Center Resources and Public Transit SCRPT 0.48 $16.23 0.13 Texoma Area Paratransit System TAPS 0.44 $18.00 0.13 Cleburne CLEB 0.29 $23.75 0.14 Services Program for Aging Needs SPAN 0.37 $24.38 0.11 The Transit System, Inc. TTS 0.28 $33.92 0.11 Gulf Coast Center GCC 0.26 $37.18 0.10 Peer Group Average

0.42 $20.15 0.14

South Padre Island SPI 0.41 $ 2.45 1.45


CLEB

CCART

CSI

FBC

GCC

KART

PTS

SCRPT SPAN

TAPS

TTS

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.10 0.20 0.30 0.40 0.50 0.60

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42

Rural Peer Group 4

Rural Peer Group 4 is comprised of five rural transit districts (see Table 20). Table 20 is sorted from the lowest operating expense per passenger trip of $12.53 to the highest of $32.45. One transit district in Peer Group 4, the Capital Area Rural Transportation System, has the lowest operating expense per passenger trip and performs above the peer group average in both operational effectiveness and operational efficiency. Figure 10 illustrates transit districts in Peer Group 4 that perform above the peer average for effectiveness and/or efficiency measures. Brazos Transit District and the Capital Area Rural Transportation System are the two Peer Group 4 rural transit districts with higher performance for operating effectiveness: Peer Group 4 rural transit districts with higher performance for operating efficiency are:

Capital Area Rural Transportation System. Alamo Area Council of Governments. West Texas Opportunities, Inc.




Cost Effectiveness


Trip



Mile Capital Area Rural Transportation System CARTS 0.43 $12.53 0.19 Brazos Transit District BTD 0.26 $13.34 0.29 Alamo Area Council of Governments AACOG 0.41 $28.03 0.09 West Texas Opportunities, Inc. WTO 0.39 $31.32 0.08 East Texas Council of Governments ETCOG 0.33 $32.45 0.09 Peer Group Average

0.37 $23.53 0.15

43


Rural Peer Group 5

Rural Peer Group 5 is comprised of four rural transit districts (see Table 21). Table 21 is sorted from the lowest operating expense per passenger trip of $8.13 to the highest of $17.06. None of the rural transit districts in this peer group perform above the peer group average for both operational effectiveness and efficiency. The Webb County Community Action Agency has the lowest operating expense per passenger trip. Figure 11 illustrates those transit districts in Peer Group 5 that perform above the peer average for operating effectiveness or operating efficiency measures. Webb County Community Action Agency and the Community Action County of South Texas are the two Peer Group 5 rural transit districts with higher performance (above the peer group average) for operating effectiveness. El Paso County and the Community Council of Southwest Texas are the two Peer Group 5 rural transit districts with higher performance (above the peer group average) for operating efficiency.

AACOG

BTD

CARTS

ETCOG

WTO

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.10 0.20 0.30 0.40 0.50 0.60

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44



Operating

Efficiency

Revenue

Miles per

Operating

Expense

Cost

Effectiveness

Operating

Expense per

Passenger

Trip

Operating

Effectiveness

Passenger Trips

per Revenue

Mile

Webb County Community Action Agency WEBB 0.32 $8.13 0.38 El Paso County EPC 0.38 $10.24 0.26 Community Action Council of South Texas CACST 0.21 $12.39 0.38 Community Council of Southwest Texas CCSWT 0.46 $17.06 0.13 Peer Group Average

0.34 $11.95 0.29


CACST

CCSWT

EPC

WEBB

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.10 0.20 0.30 0.40 0.50 0.60

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Efficiency


45

Urban Transit District Effectiveness and Efficiency Measures by Peer Group

Urban Peer Group 1

The first urban peer group is comprised of eight urban transit districts (see Table 22). Table 22 is sorted from the lowest operating expense per passenger trip of $5.02 to the highest of $16.60. None of the urban transit districts in Peer Group 1 performs above the peer group average for both operational effectiveness and efficiency. Texarkana has the lowest operating expense per passenger trip. Figure 12 illustrates those transit districts in Peer Group 1 that perform above the peer average for operating effectiveness or operating efficiency measures. Peer Group 1 urban transit districts with higher performance (above the peer group average) for operating effectiveness are:

Texarkana. Beaumont. Tyler. Waco. Longview.

The Peer Group 1 urban transit district with a higher performance (above the peer group average) for operating efficiency is Sherman-Denison. Sherman-Denison represents an outlier for operating efficiency, meaning the indicator is significantly higher than that of other transit districts in the peer group. Sherman-Denison represents higher performance for operating efficiency but lower performance for operating effectiveness (passenger trips per revenue mile). Lower operating effectiveness is because transit service in Sherman-Denison in 2009 was largely demand response (lower productivity per mile of service than fixed route). Other outliers in Peer Group 1 are Port Arthur for low revenue miles per operating expense (cost efficiency) and higher cost per passenger trip (cost effectiveness) and Temple for lower operating effectiveness (passenger trips per revenue mile).

46

Table 22. Urban Peer Group 1—Effectiveness and Efficiency Measures.




Cost Effectiveness


Passenger Trip



Texarkana TXA 0.27 $5.02 0.75 Sherman-Denison SHR-DEN 0.62 $6.46 0.25 Waco WACO 0.25 $6.64 0.60 Tyler TYL 0.23 $6.79 0.63 Longview LNG 0.25 $6.95 0.57 Beaumont BMT 0.20 $7.21 0.70 Temple TMP 0.28 $12.84 0.28 Port Arthur PA 0.16 $16.60 0.37 Peer Group Average

0.28 $8.56 0.52

Figure 12. Urban Peer Group 1—Effectiveness and Efficiency Measures.

BMT

LNG

PA TMP

WACO

SHR-DEN

TXA TYL

-

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

- 0.10 0.20 0.30 0.40 0.50 0.60 0.70

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Urban Peer Group 2

Urban Peer Group 2 is comprised of 11 urban transit districts (see Table 23). Table 23 is sorted from the lowest operating expense per passenger trip of $1.84 to the highest of $55.02. Two transit districts in Peer Group 2, Abilene and Wichita Falls, perform just above the peer group average for both operational effectiveness and operational efficiency. Figure 13 illustrates those transit districts in Peer Group 2 that perform above the peer average for operating effectiveness and/or operating efficiency measures. Peer Group 2 urban transit districts with higher performance (above the peer group average) for operating effectiveness are:

College Station-Bryan. Lubbock. Abilene. Wichita Falls.

Both Texas City-La Marque and Lake Jackson-Angleton represent outliers for low operating effectiveness (passenger trips per revenue mile) and poor cost effectiveness (cost per passenger trip). Outliers are significantly out of line with the performance indicators for other transit districts in the peer group. Low operating effectiveness in Texas City-La Marque and Lake Jackson-Angleton was because transit services were generally demand response and relatively low ridership. Flexible routes were initiated in Texas City-La Marque in 2009 and in Lake Jackson-Angleton in 2010.

Peer Group 2 urban transit districts with higher performance for operating efficiency are:

Abilene. Wichita Falls. Victoria. San Angelo.

The average cost per passenger trip for Peer Group 2 is higher because of the costs of Lake Jackson-Angleton and Texas City-La Marque ($55.02 and $25.57 per passenger trip, respectively). Both cities represent outliers for cost effectiveness because ridership is low, driving up cost per passenger.

48





Expense

Cost Effectiveness


Trip



College Station-Bryan CS-BRY 0.28 $1.84 1.95 Lubbock LUB 0.25 $3.35 1.19 Wichita Falls WICH 0.37 $4.29 0.63 Abilene ABI 0.35 $4.34 0.65 Victoria VIC 0.38 $5.85 0.45 San Angelo SANG 0.37 $6.56 0.41 Midland-Odessa MID-ODS 0.26 $7.54 0.51 Killeen KIL 0.30 $9.59 0.34 Amarillo AMA 0.23 $10.95 0.40 Texas City-La Marque TC-LM 0.30 $25.57 0.13 Lake Jackson-Angleton LJ-ANG 0.23 $55.02 0.08 Peer Group Average

0.30 $12.26 0.61


ABI

AMA

LUB

VIC

WICH

CS-BRY

SANG KIL

LJ-ANG

MID-ODS

TC-LM

-

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

- 0.10 0.20 0.30 0.40 0.50 0.60 0.70

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Urban Peer Group 3

Urban Peer Group 3 is comprised of two urban transit districts (see Table 24). As illustrated in Figure 14, The Woodlands performs above the peer average in operating effectiveness and McKinney above the peer average in operating efficiency. The Woodlands operating expense per passenger trip is $5.04, reflecting the higher passenger trips per revenue mile for The Woodlands Express commuter transit system.




Cost Effectiveness


Trip


Passenger Trips per

Revenue Mile The Woodlands TW 0.20 $5.04 0.99 McKinney MCK 0.42 $11.14 0.21 Peer Group Average

0.31 $8.09 0.60


MCK

TW

-

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

- 0.10 0.20 0.30 0.40 0.50 0.60 0.70

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Urban Peer Group 4

Urban Peer Group 4 is comprised of five urban transit districts (see Table 25). Table 25 is sorted from the lowest operating expense per passenger trip of $3.05 to the highest of $49.51. No transit district in Peer Group 4 performs above the peer group average for both operational effectiveness and operational efficiency. Figure 15 illustrates those transit districts in Peer Group 4 that perform above the peer average for operating effectiveness or operating efficiency measures. Peer Group 4 urban transit districts with higher performance (above the peer group average) for operating effectiveness are:

Laredo. Brownsville. Galveston.

Harlingen-San Benito represents an outlier for operating effectiveness and cost effectiveness. The passenger trips per revenue mile for Harlingen-San Benito (0.10 passengers per revenue mile) is significantly lower than that of other transit districts in the peer group, and the cost per passenger trip for Harlingen-San Benito ($49.51 per passenger trip) is significantly higher than that of others in the peer group. Low operating effectiveness is due to minimum levels of transit service and low ridership in the Harlingen-San Benito urban area. McAllen and Harlingen-San Benito are the two Peer Group 4 urban transit districts with higher performance for operating efficiency.





Expense

Cost Effectiveness


Passenger Trip



Laredo LAR 0.16 $3.05 2.06 Brownsville BRWN 0.15 $3.99 1.69 Galveston GALV 0.12 $4.95 1.63 McAllen MCA 0.28 $6.76 0.53 Harlingen-San Benito HARL 0.20 $49.51 0.10 Peer Group Average

0.18 $13.65 1.20

51


Urban Peer Group for Limited Eligibility Peers

The Urban Peer Group for Limited Eligibility Peers is comprised of four urban transit districts that provide transit service only for seniors and people with disabilities (see Table 26). Table 26 is sorted from the lowest operating expense per passenger trip of $14.23 to the highest of $25.93. No transit district in this Limited Eligibility Peer Group performs above the peer group average for both operational effectiveness and operational efficiency. Figure 16 illustrates those transit districts in the Limited Eligibility Peer Group that perform above the peer average for operating effectiveness or operating efficiency measures. The Limited Eligibility Peer Group urban transit district with higher performance (above the peer group average) for operating effectiveness is Grand Prairie. Mesquite and NETS are the two Limited Eligibility Peer Group urban transit districts with higher performance (above the peer group average) for operating efficiency.

BRWN

GALV

LAR

HARL

MCA

-

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

- 0.10 0.20 0.30 0.40 0.50 0.60 0.70

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Table 26. Limited Eligibility Urban Peers—Effectiveness and Efficiency Measures.




Expense

Cost Effectiveness


Passenger Trip



Grand Prairie GP 0.24 $14.23 0.30 Mesquite MTED 0.40 $16.99 0.15 Arlington ARL 0.26 $24.95 0.16 NETS NETS 0.43 $25.93 0.09 Peer Group Average 0.33 $20.53 0.17

Figure 16. Limited Eligibility Urban Peers—Comparison to Peer Group Average.

GPMTED

ARL NETS

0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

2.25

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70

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Effe

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Operating Expense per Revenue MileEfficiency


53

COMPARISON OF OPERATING EFFECTIVENESS AND EFFICIENCY ACROSS

TRANSIT DISTRICTS

Since the average performance across peer groups did not vary substantially (with the one exception among the urban systems), researchers approached identification of high performers from a total system perspective rather than from a peer group perspective. To compare operating effectiveness and efficiency across all rural transit districts and all urban transit districts, researchers plotted these measures as shown in Figure 17 and Figure 18. To identify those transit districts that are performing at a high effectiveness or a high efficiency level, researchers identified those transit districts with measures above average (as shown in the shaded areas of Figure 17 and Figure 18). Researchers considered these transit districts with either a higher operating effectiveness measure or higher operating efficiency measure (or both) for case study opportunities. Effectiveness and efficiency measures by transit district are summarized in Appendix C.

Figure 17. Rural Transit District Operating Effectiveness and Efficiency.

ASBDC

WTO

AACOGHOTCOG

ETCOG PTS

GCC CCART

TTS SPANBCAA

CTRTD TAPS CCSWT

SCRPTSPCAA GCRPCLRGVDC

CLEB CVT CCAASETRPC KART

CARTS RPMC

HCTD CSIFBC

CONCHO

DR EPCKCHS

BTDAKTXCOG

PCS REAL

CACST WEBB

-

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

- 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70

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Figure 18. Urban Transit District Operating Effectiveness and Efficiency.

These data suggest that the following agencies should be used for rural case studies:

Rural (efficiency): o Ark-Tex Council of Governments. o Collin County Area Regional Transportation. o Golden Crescent Regional Planning Commission. o Heart of Texas Council of Governments. o Public Transit Services.

Rural (effectiveness):

o Ark-Tex Council of Governments. o Brazos Transit District. o Community Action Council of South Texas. o Kleburg County Human Services. o Panhandle Community Services. o Rural Economic Assistance League. o Webb County Community Action Agency.

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Two systems were removed from case study consideration due to ongoing efforts to improve data quality—Collin County Area Regional Transportation and the Community Action Council of South Texas. Note that only the Ark-Tex Council of Governments appears in both rural lists of having both efficiency and effectiveness measures over the peer average. The following agencies should be used for urban case studies:

Urban (efficiency): o McKinney. o San Angelo. o Sherman-Denison. o Victoria. o Wichita Falls.

Urban (effectiveness):

o Brownsville. o College Station-Bryan. o Galveston. o Laredo.

McKinney, like Collin County Area Regional Transportation, was removed from consideration. Researchers selected case studies from the identified high performers with the goal of better understanding why these rural and urban transit districts perform well in efficiency and/or effectiveness. In this way, key elements that drive efficiency and those that drive effectiveness may be better isolated. Researchers chose the following transit districts as case studies:

Ark-Tex Council of Governments. Brownsville. Golden Crescent Regional Planning Commission/Victoria Transit. Heart of Texas Council of Governments. Panhandle Community Services. San Angelo. Sherman-Denison.

Figure 19 illustrates the selected case study service area and location.

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Figure 19. Case Study Service Area and Location.

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CHAPTER 5: CASE STUDIES OF EFFICIENT AND EFFECTIVE AGENCIES

The purpose of this chapter is to document case study best practices and specific strategies that are being used by successful urban and rural transit providers to achieve high performance. The goal of this chapter is to provide rural and urban transit districts with information to better understand and set targets for performance, increasing the return on federal and state transit investment. This chapter is organized into two sections. In the first section, case studies of the identified transit districts from Chapter 4 are described for transferrable elements of high performance that may provide transit districts information applicable to improve their own performance. The second section categorizes performance strategies into four categories that impact operational effectiveness and efficiency.

DESCRIPTION OF CASE STUDIES

The case studies selected represent a variety of service delivery and environmental influences impacting performance. Researchers contacted case study transit district staff to ask a series of fact-finding questions. Researchers divided fact-finding questions into four subject categories that pertain to factors that may influence operational effectiveness and efficiency. The four categories include:

Transit environment. Service design and delivery. Service policy and procedures. operating costs.

Appendix D provides the list of questions. Not all questions were asked of each transit district. For example, if the transit district did not use technology to schedule trips, researchers did not ask questions pertaining to technology.

Panhandle Community Services (High Operational Effectiveness)

Panhandle Community Services (PCS) is a Section 5311 rural transit grant recipient. PCS is part of a larger community service agency and serves a 26-county service area of over 25,749 square miles and has a population of 223,550 (2000 U.S. Census) and a projected population of 235,386—a 5.3 percent projected growth. PCS provided 300,056 annual passenger boardings in fiscal year 2009, operating 985,861 revenue miles with $2.62 million in operating expenditures. PCS’s operational effectiveness was 0.30 in fiscal year 2009 as compared to the rural average of 0.17 (excluding South Padre Island). The PCS service area includes a diverse agriculture-based economy including farm- and ranch-related industries that has seen consistent employment. The counties of Castro and Deaf Smith are reported to have the most head of cattle in the United States. The least demand for transit service is in Roberts County where there is a higher income level and low density. The major

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generators of service are reported to be meat and bi-product processing plants; major medical facilities in Amarillo, Lubbock, and Wichita Falls; dialysis centers; local colleges; and Workforce Solutions. PCS operates a demand response service only, with no fixed-route service. Demand response transit service operates using a pre-scheduled reservation system where patrons call in advance and request a pick-up and drop-off at their choice of location within a service area. Approximately 50 percent of the patrons are subscription trips. Subscription service is service for which patron trips that have the same origin and destination (typically work trips) are automatically scheduled; the patron is not required to call in for a reservation. All trips are shared ride trips not dedicated to one trip type. The PCS Board consists of 15 members from the 26 counties and are all local officials reported to be transit supportive and active in board decisions. PCS works closely with the community and regularly attends community and town hall meetings. Community meetings have recently resulted in coordination with two plants—Cargill Meat Solutions in Parmer County and Tejas Bi-Products in Deaf Smith County. These plants employ 1,600 workers per shift. These plants worked with PCS to align transit with worker shifts to allow PCS to effectively serve both plants through subscription service. PCS reports that worker turnover at these plants was drastically reduced (over 30 percent drop) when reliable public transportation was introduced. PCS receives funding for service from federal and state Section 5311 funding, Section 5316 Job Access Reverse Commute (JARC) funds, passenger fares, and a variety of contract revenues. Agencies that contract for service with PCS include American Medical Response, the Veterans Administration, Pantex Employee, Tejas Industries, and Cargill Industries. PCS stations vehicles at 10 locations throughout its 26-county area to reduce mileage to the first patron pick-up and from the last patron drop-off point of the day (referred to as deadhead miles). At least one spare vehicle is stationed at these locations during the day to provide switch-out vehicles in case of a vehicle breakdown or to operate a smaller/larger-size vehicle for different peak times of the day. PCS uses computer-assisted scheduling/dispatching and routing software, mobile data computers (MDCs), automatic vehicle locator systems, and cell phones to schedule, dispatch, and communicate. Prior to the day of service, patrons throughout the region call a 1-800 telephone number to schedule a trip. When reserving their first trip, PCS creates a client profile in the scheduling system to facilitate the reservation process. When a reservation is requested, the scheduling system provides a list of suggested trips. The suggested trips are a result of the scheduling system’s optimizing algorithm based on parameters PCS inputs into the system including cost per labor hour, overtime rates, number of vehicles available, number of drivers available, capacity of vehicle, and wheelchair capacity. Trips are automatically scheduled, and each area dispatcher reviews trips within his or her portion of the region for reasonableness. The dispatchers then provide suggested changes to the central Routemaster, who makes the final change decision and creates the final schedule.

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On the day of service, drivers download scheduled trips sorted by estimated time of arrival through the MDC during their pre-trip inspection and then initialize their odometer miles. Paper manifests (driver trip schedules) are printed for Medicaid trips only where the patron is required to provide a signature. Drivers log arrivals, departures, and no-shows through MDCs. These data then update the scheduling system in real time. Dispatchers use the automatic vehicle locator system for a variety of functions including identifying the closest vehicle to a waiting patron, estimating the vehicle arrival time for patron pick-up, and determining the direction and speed that a vehicle is moving. PCS relies on MDC text messages to communicate with drivers and uses cell phones only when voice communication is needed. The scheduling system provides a visual of the slack in the system—usable time that is not yet scheduled. This provides the dispatcher options when trips are running late and may be moved to another vehicle to maintain on-time performance. PCS allows same-day reservations if the schedule allows. At the end of the day, drivers input ending mileage into the MDC. A program detects data entry errors in mileage, allowing mileage to be corrected by administrative staff. Drivers turn in fares collected to the finance department. The finance department prints a report of the cash fares and contract fares by driver to reconcile and provides a receipt to the driver with discrepancies reported to the supervisor for review. The scheduling and dispatching function was centralized, moving 10 regional dispatchers to be housed in one location. PCS reports that productivity has increased because dispatchers are better able to communicate to resolve trip issues, readily transferring trips between vehicles to prevent late trips, and because they can see the big picture. PCS cross-trains staff, partnering a Routemaster with new employees, and provides training for all staff four to five times annually. Because PCS cross-trains staff to serve in multiple functions, PCS looks for computer-savvy employees. PCS recommends a strong network administrator when operating an automated scheduling/dispatching, automated vehicle location, and mobile data computer system that provides for a paperless operation. Lessons from PCS include:

Ensure the active support of a transit board made of local officials. Attend and participate in community meetings. Work closely with employment centers to accommodate worker shifts. Decentralize the transit fleet. Invest in technology. Centralize scheduling/dispatching. Cross-train staff.

Heart of Texas Council of Governments (High Operational Efficiency)

The Heart of Texas Council of Governments (HOTCOG) is a Section 5311 rural transit grant recipient. HOTCOG serves a six-county service area over 5,478 square miles and has a population of 168,338 (2000 U.S. Census) with a projected 2010 population of 180,734—a 7.4 percent projected growth. HOTCOG provided 56,251 annual passenger boardings in fiscal

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year 2009, operating 619,091 revenue miles with $1.16 million in operating expenditures. HOTCOG’s operational efficiency was 0.53 in fiscal year 2009 as compared to the rural average of 0.38 (excluding South Padre Island). HOTCOG serves the rural portion of McLennan County, and Waco Transit serves the urban portion (Waco urbanized area) of McLennan County. The Waco urbanized area is a major destination for HOTCOG patrons to access medical facilities, the Texas Workforce Center, Baylor University, McLennan Community College, and Texas State Technical College.

HOTCOG contracts with four service providers to provide service throughout its six-county service area. Falls County is described as the most economically distressed county in HOTCOG’s service area. An initiative called 6 to Success provides a transit service operated in cooperation with HOTCOG and Waco Transit (16). This initiative was planned through the Heart of Texas Workforce Board through the Highway 6 Regional Transportation Project. This route connects rural areas to the urban Waco Transit system where riders can access employment, education, and other destinations. HOTCOG provides service from Falls County into the Waco service area. Waco Transit provides service to the 6 to Success route from Waco into Falls County. Three major poultry processing plants—Pilgrim’s Pride, Sanderson Farms, and Cargill—are included along the 6 to Success transit route and five small cities.

HOTCOG also provides demand response service throughout the remaining service area. Outside of Waco, major generators of transit service include Hill College in Bosque County and service to primary and intermediate schools throughout the counties. HOTCOG provided 56,251 passenger boardings in fiscal year 2009. The number of trips originating in each county is as follows:

Bosque: 6,207. Hill: 5,095. McLennan: 12,676. Falls: 10,146. Limestone: 18,496. Freestone: 3,631.

HOTCOG receives funding for service from federal and state Section 5311 funding, local contributions, Section 5310 elderly and individuals with disabilities funds, and passenger fares. HOTCOG relies on its subcontractors to effectively schedule and dispatch, and is working to coordinate service among these subcontractors to avoid duplicative service. (HOTCOG plans to have an automated scheduling/dispatching system and mobile data computers operational in FY 2011.) Drivers receive their manifests from the site offices where their vehicles are housed. The vehicles are housed in locations that are closest to the most populated towns in each of the counties. They receive changes/modifications to the schedules throughout the day via cell phone. At the end of the day, drivers return the manifests at the site office. The site manager or transportation director reviews the manifests for correctness and completeness.

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HOTCOG allows same-day ―will call‖ reservations mainly on return trips when the patron does not know the exact pick-up time (i.e., from a doctor’s office). Patrons may reserve subscription (standing) reservations for repeat trips on the same days, times, and locations. As noted above, HOTCOG contracts the operation of its service to four subcontractors:

Central Texas Senior Ministry. Bosque County Transit. Freestone County Transit. Limestone County Transit.

HOTCOG negotiates rates with each of its subcontractors for the operation and maintenance (including fuel) of transit service. HOTCOG’s purchase of service expenditures in fiscal year 2009 were $902,429, for an average purchase of service expenditure per boarding of $16.04. Table 27 provides the fiscal year 2009 purchase of service expenditures by contractor. Table 27. HOTCOG Fiscal Year 2009 Purchase of Service Expenditures by Subcontractor.

Subcontractor Operating

Expenditures Central Texas Senior Ministry $424,247 Bosque County Transit $96,114 Freestone County Transit $67,830 Limestone County Transit $314,238 Total Purchase of Service Expenditure $902,429 Total Passenger Boardings 56,251 Average Subcontractor Expenditure per Boarding $16.04

In fiscal year 2009, HOTCOG maintenance cost was part of the lease agreement with the subcontractors. In fiscal year 2010, HOTCOG received funding through TxDOT to contract with Waco Transit to provide vehicle maintenance. HOTCOG and Waco Transit have a memorandum of understanding to provide centralized maintenance at the Waco Maintenance Facility. The goals of this arrangement are to assess the status of the fleet condition, standardize maintenance costs for the six-county region, standardize maintenance records, decrease vehicle downtime, provide consistent wheelchair-lift diagnostics/repair, track warranty recovery, and maximize the useful fleet life. Lessons from HOTCOG include:

Sub-contract service to local transit providers. Plan and develop service with Workforce Solutions, businesses, schools, cities,

community organizations, and adjacent urban transit district. Arrange for vehicle maintenance through a memorandum of understanding with the

urban transit district.

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Ark-Tex Council of Governments (High Operational Effectiveness and Efficiency)

The Ark-Tex Council of Governments (Ark-Tex) is a Section 5311 rural transit grant recipient. Ark-Tex serves a nine-county service area over 5,761 square miles and has a population of 221,701 (2000 U.S. Census) with a projected 2010 population of 230,739—a 4.1 percent growth. Ark-Tex provided 394,657 annual passenger boardings in fiscal year 2009, operating 1,312,82 revenue miles with $2.37 million in operating expenditures. Ark-Tex’s operational effectiveness was 0.30, and operating efficiency was 0.55 in fiscal year 2009, as compared to the rural average of 0.17 and 0.38, respectively (excluding South Padre Island). Ark-Tex receives funding for service from federal and state Section 5311 funding, local contributions, in-kind contributions, the Department of Aging and Disabilities, the Department of State Health Services, Section 5310 elderly and individuals with disabilities funds, Section 5316 Job Access Reverse Commute (JARC) funds, and passenger fares. Revenues are also generated from a variety of contract revenues including the Veterans Administration, North East Texas Community College, the Texarkana Vocational Technical Institute, and Opportunities Incorporated. The Ark-Tex service area is an agriculture-based economy including farm- and ranch-related industries. Pilgrim’s Pride headquartered in East Texas filed for bankruptcy in December 2008, resulting in nine poultry-plant closings in the area. These poultry processing plant closings significantly impacted employment and transit ridership serving these plants—general public ridership dropped over 20 percent from fiscal year 2008 to 2009. Ark-Tex adjusted its service supply with the drop in demand, maintaining good service productivity. Other major generators of service include orchard farms, Workforce Solutions, the Red River Army Depot, colleges and universities, medical facilities, and a variety of manufacturing plants. Transit staff at Ark-Tex exerted effort to connect to community transit needs and work with employers. Being a part of a council of governments provides an advantage in staying connected to community needs. Table 28 provides major generators of service and passenger boardings by county in the Ark-Tex transit service area.

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Table 28. Ark-Tex Council of Governments Major Generators of Service by County.

County Major Transit Service Generators Passenger Boardings

Bowie County (22% of ridership)

Red River Army Depot, Christus St. Michael’s Hospital, Wadley Hospital, Texas A&M University, Texarkana College, Sterno and Colgate Palmolive manufacturing plants, Workforce Solutions

86,826

Titus County (20% of ridership)

Titus County Memorial Hospital, Pilgrim’s Pride Rendering Plant, Northeast Texas Community College, Pittsburgh Hotlink Plant, tortilla factories

78,931

Lamar County (18% of ridership)

Paris Junior College, Paris Regional Medical Center, Campbell Soup, Earth Grain Foods, Sara Lee, MacIntosh Cloth

71,038

Hopkins County (15% of ridership)

Hopkins County Memorial Hospital, Torro Chainsaw plant, Pilgrim’s Pride rendering plant, major industrial park

59,199

Cass County (8% of ridership)

Atlanta Memorial Hospital, Evinrude Motors 31,572

Red River County (6% of ridership)

Chainsaw and casket manufacturers 23,679

Franklin County (4% of ridership)

Strip mining—limited generators of service 15,786

Morris County (5% of ridership)

Lone Star Steel, lumber manufacturing 19,733

Delta County (2% of ridership)

Lake area—no major generators of service 7,893

Total 394,657 Ark-Tex directly operates demand response service in four counties and contracts with Northeast Texas Opportunities (NETO) to provide service in the western counties. The NETO service contract is a 10-year request for proposal contract and paid on a cost reimbursement basis. Ark-Tex also has a memorandum of understand with NETO and two taxicab companies—Yellow Cab of Paris and City Cab of Texarkana—to provide Section 5316 JARC and Section 5317 New Freedom service. These services are paid on a trip-by-trip basis set at a negotiated rate of $6.00 within the city limits of Texarkana and Paris; $10.00 within the counties of Lamar, Bowie, Red River, Titus, Morris, and Hopkins; and $12.00 county to county. The taximeter fare is not taken into account. Ark-Tex houses vehicles throughout its nine-county service area and typically has one spare vehicle in each area to cover vehicle breakdowns or other immediate needs. Ark-Tex has some maintenance conducted at the Regional Maintenance Facility located in Mt. Pleasant and also has agreements with maintenance vendors in all nine counties that provide maintenance and in-kind wrecker services. Ark-Tex procured an automated scheduling/dispatching and mobile data computer system in fiscal year 2010. Ark-Tex relies on cell phones and radios to communicate with drivers. Ark-Tex uses fleet maintenance software to track vehicle maintenance, report on

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vehicle reliability, and prompt scheduling for preventive maintenance. With the installment of the mobile data computer system, drivers will receive and complete a Daily Vehicle Inspection Report upon morning log-in. This report will be uploaded to the Regional Maintenance Facility for review. The maintenance facility will have a locator map to track vehicles. Ark-Tex allows patrons to schedule trips as much as one month to as little as one day in advance. Same-day service is accommodated if the schedule allows. The agency creates a profile for each patron when he or she first uses the service and provides rules and procedures. Reservations are currently taken at offices across nine counties. Schedules are created for the vehicles assigned in each area. When the new automated scheduling/dispatching system is implemented, there will be one 866 phone number for the entire service area. Because the current Texarkana office does not have the ability to house the entire dispatch/scheduling/reservation staff, initially patron calls will be routed to two dispatch/reservation centers—one located in Texarkana and one in Paris. Patron calls from the counties of Bowie, Cass, Morris, Titus, and Franklin will be routed to the Texarkana office, and calls from the counties of Lamar, Delta, Red River, and Hopkins will be routed to the Paris office. At 4:00 p.m., passenger trips from the two offices will be combined with the Texarkana office, creating the final driver manifests (schedules). These schedules will be downloaded to the respective driver’s mobile data computer units, with the final schedule sent to the Paris office. A new transportation facility located in Texarkana is planned to accommodate the entire staff in the future with dispatch/scheduling/reservations completely centralized. Ark-Tex controls fuel costs using a private fuel card company and also purchases fuel through county agreements with Red River, Hopkins, and Titus Counties. Ark-Tex drivers’ beginning wage is approximately $9.00 per hour with no commercial driver license (CDL) and $10 with a CDL. The highest paid driver at Ark-Tex earns approximately $11.50 per hour. No overtime is allowed, only compensatory time due to the Ark-Tex Council of Governments’ policy. The majority of Ark-Tex drivers are full-time—49 full-time drivers and 5 part-time drivers. Full-time drivers receive 100 percent paid health, dental, and vision benefits and a plan for retirement. Ark-Tex employs nine full-time dispatchers/schedulers, four supervisors, two mechanics, and six administrative staff. Ark-Tex also has some volunteer dispatchers. Lessons from Ark-Tex include:

Take advantage of council of government community outreach programs. Work closely with employment centers to accommodate worker shifts. Adjust service supply to match service demand. Contract JARC and New Freedom on a trip-by-trip basis to control costs. Negotiate long-term contracts for service in western counties to lower rates. Arrange agreements with maintenance vendors in all counties served. Purchase fuel through fuel cards and interlocal agreements with county governments. Establish driver wage rates at a competitive but conservative level.

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Golden Crescent Regional Planning Commission (High Operational Efficiency)

The Golden Crescent Regional Planning Commission (GCRPC) is a Section 5311 rural transit grant recipient. GCRPC serves an eight-county service area over 7,088 square miles and has a population of 160,333 (2000 U.S. Census) with a projected 2010 population of 169,456—a 5.6 percent projected growth. GCRPC provided 136,619 annual passenger boardings in fiscal year 2009 over 1,027,494 revenue miles with $1.94 million in operating expenditures. Operational efficiency was 0.53 in fiscal year 2009 as compared to the rural average of 0.38 (excluding South Padre Island). GCRPC receives funding for service from federal and state Section 5311 funding, local contributions, in-kind contributions, the Medical Transportation Program, the Department of Aging and Disabilities, Section 5310 elderly and individuals with disabilities funds, Section 5316 JARC funds, and passenger fares. The GCRPC transit service area has seen a steady growth in unemployment, hitting a high of 8.1 percent in the first half of 2010. Loss of jobs in the construction, manufacturing, and natural resources (oil and gas) industries has had negative effects on the economy. The major generators of service in the GCRPC rural transit district area are the attractors of transit trips (employers, medical facilities, retail businesses) within the Victoria urbanized area. The City of Victoria is the designated recipient of the Victoria urbanized area transit funds and has an agreement with GCRPC to provide transit service. Major generators of service include three hospitals and medical specialists located in Victoria. Victoria is also a major destination for shopping/retail, restaurants, and colleges/university. GCRPC rural service is provided in an eight-county area. The rural general public service is called RTRANSIT, is a demand response service, and requires a 24-hour advance reservation. GCRPC also provides demand response medical transportation program (MTP) service for Medicaid recipients under a contract with the Texas Health and Human Services Commission. MTP offers non-emergency medical transportation services under constrained service delivery guidelines. Reimbursement for MTP services is based on a pre-established rate per passenger trip. GCRPC is reimbursed $23.65 for routine MTP trips and $79.50 for special MTP trips. Special MTP trips are those that cross county lines. GCRPC also oversees a commuter vanpool program to the Inteplast Plant in Jackson County. The vanpool program offers three routes from Victoria, Port Lavaca, and Bay City. The vanpool program carried 13,051 passengers in fiscal year 2009. GCRPC directly operates all services within Victoria and Dewitt Counties. This includes Victoria Transit, RTRANSIT, and MTP services in those two counties. In each of the remaining six counties in its region, GCRPC contracts for provision of RTRANSIT and MTP services as follows:

Calhoun County—Calhoun County Senior Citizens Association (SCA), Inc. Goliad County—Goliad County. Gonzales County—Gonzales County SCA, Inc. Jackson County—Friends of Elder Citizens, Inc.

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Lavaca County—Lavaca County. Matagorda County—Friends of Elder Citizens, Inc.

Matagorda County is not part of the designated GCRPC region but is served under contract to the organization that also serves Jackson County. Figure 20 shows the service area by provider.

Figure 20. Golden Crescent Regional Planning Commission Transit Providers.

The following lists the number of trips originating in each county that GCRPC serves:

Victoria: 27,205. Lavaca: 26,388. Matagorda: 25,374. Calhoun: 18,511. Gonzales: 17,078. Jackson: 10,371. Goliad: 6,150. Dewitt: 5,542.

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GCRPC does provide subscription service and allows same-day reservations where schedules can accommodate. GCRPC directly operated service is scheduled through an automated scheduling/dispatching system. No mobile data computer or automated vehicle location system is used. GCRPC recently installed an automated scheduling/dispatching system at its subcontractors. Not all subcontractors use the automated scheduling/dispatching due to connectivity issues. Vehicles are housed nearest to transit demand to minimize deadhead miles. GCRPC partners with cities, counties, and school districts to use secured parking facilities (in-kind contribution) to park vehicles.

GCRPC directly operated service has a large part-time driver contingent with approximately 50 percent of all drivers being part-time. Driver wages begin at $9.00 with the maximum driver wage at $11.71. Overtime is controlled by having a large part-time staff. Full-time drivers are provided 100 percent paid health benefits and life benefits. GCRPC pays its subcontractors on a cost-per-trip basis. GCRPC has 12 full-time and 4 part-time dispatchers/schedulers/ reservationist, 10 supervisors, 12 administrative positions, and 2 full-time-equivalent GCRPC overhead staff. Lessons from the Golden Crescent Regional Planning Commission include:

Set wage rates at a moderate level and establish a cap on wage rate increases. Increase the percent of part-time drivers. Contract with local transportation providers for service in outlying areas. Reduce administrative costs and share overhead through operation of an urban and rural

transit district under one agency. Decentralize the transit fleet. Invest in technology.

Brownsville Urban System (High Effectiveness)

The City of Brownsville’s Brownsville Urban System (BUS) is a Section 5307 small urban transit grant recipient. BUS is a mass transit system based in and serving Brownsville, Texas. BUS is currently the largest mass transit system in the Rio Grande Valley. Brownsville’s urbanized area (UZA) has a population of 165,776 (2000 U.S. Census) and a projected population of 214,428 in 2010—a 29 percent projected growth. BUS serves the UZA. Brownsville is located at the southernmost tip of Texas and is a gateway for U.S.-Mexico commerce. BUS provides fixed-route bus service and ADA complementary paratransit service operating between 6:00 a.m. and 8:26 p.m. Monday through Saturday. No service is provided on Sundays and on four major holidays. All fixed-route service begins and ends at the downtown transit terminal (Figure 21). The terminal is built around the old City Hall that still houses several City of Brownsville departments.

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Image Source: http://www.waymarking.com/waymarks/WM3CWR_Brownsville_Texas

Figure 21. Brownsville Downtown Transit Terminal. In fiscal year 2009, BUS transportation provided 1,775,683 passenger trips operating 998,317 revenue miles and expended $6,537,176 in operating dollars. BUS operating effectiveness is 1.69 as compared to the 0.95 urban transit district average.

The City of Brownsville directly provides bus operations and other administration. BUS subcontracts for fleet maintenance to First Vehicle Services and subcontracts for the director and assistant director positions to First Transit, Inc. BUS receives funding for service from federal and state Section 5307 funding, local contributions, in-kind contributions, passenger fares, auxiliary transit revenues, non-transit-related revenues, Section 5310 elderly and individuals with disabilities funds, Section 5316 Job Access Reverse Commute funds, and Section 5317 New Freedom funds.

The major generator of service in the BUS transit service area includes three international crossings, two major hospitals, other medical facilities, a shopping mall, Workforce Solutions, the Port of Brownsville, educational facilities including the University of Texas at Brownsville, and health and human service agencies. According to a survey conducted by BUS in 2008, only 24 percent of BUS riders are employed, and the trip purpose, by percentage, was found to be as follows:

School: 15.5 percent. Work: 20.0 percent. Other: 5.9 percent. Visit/recreation: 11 percent. Personal: 15.5 percent. Shopping: 21.1 percent. Medical: 10.9 percent.

http://www.waymarking.com/waymarks/WM3CWR_Brownsville_Texas

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The survey reported that 66 percent of respondents use BUS more than three times a week and 61 percent have no other means of transportation. BUS staff reports that international bridge crossings are a major source of ridership, with the high traffic resulting in non-resident transit demand. According to the 2010–2035 Brownsville Metropolitan Transportation Plan, the average monthly traffic based on the first six months of fiscal year 2009 was as follows:

Veterans International Bridge: o Auto: 132,180 monthly. o Truck/commercial: 12,859 monthly. o Pedestrian: 3,741 monthly. o Bus: 697 monthly.

Brownsville and Matamoros Border Crossing: o Auto: 139,741 monthly. o Truck/commercial: 0. o Pedestrian: 50,354 monthly. o Bus: 0.

Gateway International Bridge: o Auto: 127,070 monthly. o Truck/commercial: 0. o Pedestrian: 160,339 monthly. o Bus: 0.

Lessons from BUS include:

Design transit routes and locate stops to serve the significant number of international travelers near the Texas-Mexico border.

Ensure transit routes serve all major shopping, medical facilities, education centers, and employment centers.

Coordinate bus services by connecting routes at a central bus terminal.

San Angelo—Concho Valley Transit District (High Efficiency)

The designated recipient of Section 5307 small urban transit district funds for the San Angelo urbanized area is the City of San Angelo. The City of San Angelo has an agreement with the Concho Valley Transit District (CVTD) to provide transit service in the San Angelo urbanized area. On September 1, 2006, the City of San Angelo and the Concho Valley Council of Governments (the designated Section 5311 rural transit recipient) consolidated the urban and rural public transportation system under the operation of CVTD.

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The urban transportation system is known as the TRANSA Public Transportation System. The TRANSA service area boundary is the San Angelo urbanized area, which had a population of 87,969 (2000 U.S. Census). In addition, the area is projected to have virtually no growth, with an estimated 2010 population of 87,710. TRANSA directly operates all service and has five accessible fixed-route vehicles and ADA complementary paratransit services operating Monday through Friday 6:30 a.m.–6:30 p.m. and Saturday 7:30 a.m.–6:30 p.m. (Figure 22). Passengers on the fixed route use a flag-down system and can transfer at the Santa Fe depot. Major generators of transit service include medical facilities, colleges/universities, nutrition centers, shopping centers, and social service agencies.

Figure 22. San Angelo TRANSA Fixed-Route Map.

In fiscal year 2009, TRANSA provided 207,090 passenger trips over 466,107 revenue miles with $1,556,604 in operating expenditures. TRANSA operating efficiency was 0.37 as compared to the urban transit district average of 0.24. TRANSA receives funding for service from federal and state Section 5307 grants, local contributions, passenger fares, auxiliary transit revenues, other transportation revenues, Section 5310 funds for the elderly and people with disabilities, and Area Agency on Aging contracts.

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TRANSA has the advantage of sharing costs between the urban and rural transit districts. TRANSA purchases preventive maintenance through a contract that incorporates both the urban and rural transit systems, receiving volume discounts. Administrative costs such as utilities, office space, accounting, and other administrative staff are shared between the urban and rural systems. TRANSA purchases fuel from the City of San Angelo through a local agreement with the city. TRANSA has 21 full-time drivers and 1.5 full-time-equivalent part-time drivers. Driver wages begin at $8.00. The average full-time driver wage is $10.00, and the average part-time wage is $8.76. TRANSA pays for 48 percent of the cost of health benefits for full-time staff. Lessons from San Angelo include:

Reduce administrative costs through operation of urban and rural transit districts under one agency. Negotiate volume discounts for maintenance through joint urban and rural transit district contracts.

Offer competitive but moderate driver wage rates and benefits.

Sherman-Denison—Texoma Area Paratransit System (High Efficiency)

The designated recipient of Section 5307 small urban transit district funds for the Sherman-Denison urbanized area is the Texoma Council of Governments (TCOG). TCOG, in turn, contracts with Texoma Area Paratransit System for the delivery of all services within the TCOG service area. TAPS also operates the rural system in the six counties surrounding the Sherman-Denison urbanized area and is the designated recipient of Section 5311 rural transit district funds. Thus, TAPS provides all public transportation services within the region, both rural and urban. TAPS restructured its board and now consists of city and county officials providing support and a voice for transit in the community. The Sherman-Denison urbanized area has a population of 56,168 (2000 U.S. Census) and a projected population of 62,140 in 2010—an 11 percent projected growth. The TAPS urban system provided 139,095 annual passenger boardings in fiscal year 2009, operating 436,305 revenue miles with $1.05 million in operating expenditures. TAPS urban system efficiency was 0.62 in fiscal year 2009 as compared to the urban average of 0.24. The TAPS urban system received funding in fiscal year 2009 from federal and state Section 5307 funding, local contributions, other transportation revenues, non-transit-related revenues, the Medical Transportation Program, the Department of Aging and Disabilities, Texoma Tours, and Mental Health Mental Retardation of Texoma. The TAPS executive director and staff regularly attend community meetings and frequently speak to groups at colleges, employment centers, and community outreach activities. TAPS is regularly in the media (newspapers, television, and magazines) with stories such as celebrating the opening of new routes, celebrating its financial turnaround, and celebrating receipt of its 200th bus. The TAPS executive director is a community leader who actively promotes TAPS, serves as mayor pro-tem for the City of Bonham and serves on the Grayson County College Board of Directors.

72

The TAPS urban system operated demand response transit service only in 2009 and starting in fiscal year 2010 began operating two fixed routes (Roo Route and Viking Route) serving Austin College and Grayson Community College. Other major generators of service within Sherman-Denison are primary/intermediate schools, after-school care programs, medical facilities, shopping, the Dallas Area Rapid Transit (DART) rail station, Peterbilt Motor Company, Trailblazer Blue Cross Blue Shield, Workforce Solutions, and health and human services. TAPS purchases fuel through a private fuel card contract in cooperation with Tarrant County. The cooperative purchase of fuel with Tarrant County allows TAPS to take advantage of the larger fuel quantity and therefore lower cost. TAPS estimates $0.10 to $0.12 per gallon savings in fuel costs. Additionally, the TAPS fuel card vendor provides a mechanism to track fuel and mileage to monitor fuel usage and fuel efficiency. Fuel reports are reviewed weekly. The beginning driver wage is $7.95 with an average driver wage of $8.18. Overtime is closely monitored and limited. TAPS urban and rural operation employs approximately 80 full-time drivers and 50 part-time drivers. Health benefits are provided for full-time drivers at 90 percent, and part-time drivers have the option of receiving a limited plan for $10 per paycheck. TAPS supervisory staff actively monitors driver/staff productivity and pay hours. Maintenance is provided through a partnership with a local dealership that provides preventive maintenance at a volume discount rate. The dealership provides a vehicle diagnostic with every oil change and reports on the life of parts/need of replacement. Minor repairs are conducted by two in-house mechanics and one technician. The dealership provides free towing and charges a flat mechanic rate for major repairs. Lessons from TAPS include:

Reduced administrative costs through operation of urban and rural transit districts under one agency and allocation of overhead.

Actively monitor driver/staff productivity and pay time. Partner with local maintenance providers for volume discounts. Purchase fuel through fuel cards and county agreements. Have an active/supportive transit board made of city/county elected officials. Publicize service through a variety of media outlets. Plan and develop service with colleges, employment centers, school districts, and

community organizations.

TEXAS TRANSIT DISTRICT STRATEGIES THAT IMPACT OPERATING EFFECTIVENESS AND EFFICIENCY

Researchers found through the case study research that although the environment plays some role in performance, there are other factors that management can control or influence to improve operating effectiveness and efficiency. Researchers grouped these factors into four major categories:

Efforts to grow ridership. Efforts to manage costs. Efforts to decrease vehicle miles and maximize labor productivity. Efforts to improve administration.

73

The following lists strategy factors by the four major categories.

Efforts to Grow Ridership—Improve Effectiveness

Factors that contribute to growing ridership include the following:

Engage city and county officials in transit—find champions for transit. Actively seek out areas with transit-dependent communities. Work with major manufacturers, plants, and industries to serve worker shifts. Consistently attend and actively request to speak at community events and meetings. Work with colleges, universities, and school districts to provide transit routes and create

cooperative agreements. Work with health and human services and medical facilities to serve patrons. Drive routes and monitor for new service needs.

Efforts to Manage Costs—Improve Efficiency

Factors that contribute to managing cost include:

Actively seek in-kind contributions to support transit. Work with cities and counties in supplying fuel at lower-cost bulk rates. Utilize fuel cards (state or private) to monitor fuel usage and cost. Use sub-contractors at cost-effective rates where appropriate. Utilize sub-contractors to provide service during low-demand times of day on a trip-by-

trip cost basis. Ensure contract rates are appropriate and cover both operating and capital costs. Allocate administrative and overhead costs across programs.

Efforts to Decrease Vehicle Miles and Maximize Labor Productivity—Improve Efficiency and Effectiveness

Factors that contribute to decreasing vehicle miles or maximizing labor productivity include:

Create satellite parking sites to minimize deadhead, with spares located throughout the service area (seek in-kind contributions for parking).

Create cooperative agreements with other transit districts to utilize vehicles when in other transit-district service areas to minimize downtime/idle time and maximize productivity.

Utilize scheduling systems to maximize grouping of trips and minimize slack time. Utilize vehicle locator systems to find the closest vehicles, provide quality information to

patrons, map scheduled trips to ensure trip reasonableness, and verify no-shows. Cross-train staff to provide backup and improve staff productivity (match senior staff

with new trainees). Monitor/manage driver overtime. Monitor vehicles to proactively troubleshoot late trips and take ―will-call‖ or same-day

trips to fill the slack.

74

Create both full-time and part-time driver schedules to match service demand. Group trips without dedicating vehicles to trip types—shared-ride general public service.

Efforts to Improve Administration—Improve Effectiveness and Efficiency

Factors that contribute to improving administration include:

Run weekly/monthly reports to monitor/manage driver productivity, passenger complaints, passenger no-shows/cancellations, absenteeism, vehicle inspections, vehicle repairs (repeats), client travel times, and client wait times.

Require vehicle operators to turn in paperwork and fares on a daily basis, with finance staff providing receipt and reconciliation.

Ensure quality maintenance with priority turnaround through maintenance agreements. Monitor preventive maintenance and fleet issues to prevent costly repairs. Regularly communicate to passengers rules/regulations. Create a partnership with patrons

to meet vehicles on time. Follow up with complaints quickly to nurture the patron-transit agency relationship.

75

REFERENCES

1 Federal Transit Administration. SAFETEA-LU Authorization Levels for Fiscal Years 2005

through 2009. http://www.fta.dot.gov/documents/SAFETEA-LU_Funding_by_Program_by_Year.pdf. Accessed August 26, 2010.

2 Federal Transit Administration. Grant Programs. http://www.fta.dot.gov/funding/grants_financing_263.html. Accessed August 26, 2010.

3 H. Levinson. Systems and Service Planning. In Public Transportation (Second Edition), G. Gary & L. Hoel (editors), Prentice Hall, Englewood Cliffs, New Jersey, 1992. http://www.fta.dot.gov/funding/grants_financing_263.html. Accessed August 26, 2010.

3 H. Levinson. Systems and Service Planning. In Public Transportation (Second Edition), G. Gary & L. Hoel (editors), Prentice Hall, Englewood Cliffs, New Jersey, 1992.

4 University of Pretoria. Rural Public Transportation: An On-Board Survey of Transit Users in Rural Alabama Counties: A Pilot Study, Presentation by J. Oluwoye and E. Gooding at the 25th Annual Southern African Transport Conference, July 2006. http://www.up.ac.za/dspace/bitstream/2263/6071/1/026.pdf. Accessed January 21, 2011.

5 G. Fielding. Managing Public Transit Strategically: A Comprehensive Approach to Strengthening Service and Monitoring Performance. Jossey-Bass Publishers, San Francisco, California, 1987.

6 G. Smerk and R. Gerty. Mass Transit Management: A Handbook for Small Cities. Institute for Urban Transportation, Indiana University, 1992.

7 Community Transportation Association. Rural Transit Performance Measures, L. Radow and C. Winters. http://www.ctaa.org/webmodules/webarticles/articlefiles/Rural_Transit_Performance_Measurement.pdf. Accessed January 21, 2011.

8 J. Burkhardt, B. Hamby, and A. McGavock. TCRP Report 6: Users’ Manual for Assessing Service-Delivery for Rural Passenger Transportation. Transportation Research Board, National Research Council, Washington, D.C., 1995.

9 KFH Group, Inc. TCRP Report 124: Guidebook for Measuring, Assessing, and Improving Performance of Demand-Response Transportation. Transportation Research Board of the National Academies, Transit Cooperative Research Program, Washington, D.C., 2008.

10 Florida Department of Transportation. Benchmark Rankings of Transit Systems in the United States. http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_PTO/FDOT_BC137_43.pdf. Accessed August 2010.

11 North Carolina Department of Transportation, Division of Public Transportation. Performance-Based Budgeting for North Carolina Public Transportation Systems. http://www.ncdot.gov/nctransit/download/PerformanceBasedBudgeting.pdf. Accessed August 2010.

12 Transport for London. TFL Annual Report (2003, 2004, 2005, …, 2010). http://www.tfl.gov.uk/corporate/about-tfl/investorrelations/1458.aspx.

13 P. Hendren and D. Niemeier. Identifying Peer States for Transportation System Evaluation & Policy Analysis. Transportation (2008) 35, Springer Science+Business Media, LLC, Published On-Line, 2008, pp. 445-465. http://www.springerlink.com/content/y6213mk71885k357/. Accessed August 2010.

http://www.fta.dot.gov/documents/SAFETEA-LU_Funding_by_Program_by_Year.pdf

http://www.fta.dot.gov/documents/SAFETEA-LU_Funding_by_Program_by_Year.pdf

http://www.fta.dot.gov/funding/grants_financing_263.html

http://www.up.ac.za/dspace/bitstream/2263/6071/1/026.pdf

http://www.ctaa.org/webmodules/webarticles/articlefiles/Rural_Transit_Performance_Measurement.pdf

http://www.ctaa.org/webmodules/webarticles/articlefiles/Rural_Transit_Performance_Measurement.pdf

http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_PTO/FDOT_BC137_43.pdf

http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_PTO/FDOT_BC137_43.pdf

http://www.ncdot.gov/nctransit/download/PerformanceBasedBudgeting.pdf

http://www.tfl.gov.uk/corporate/about-tfl/investorrelations/1458.aspx

http://www.springerlink.com/content/y6213mk71885k357/

76

14 D. Spears. Performance Monitoring of the 50 State Highway Systems by Means of Cluster

Analysis, Master Thesis. Department of Political Science, University of North Carolina, Charlotte, North Carolina, 1995.

15 P. Rylus, K. Coffel, J. Parks, V. Perk, L. Cherrington, J. Arndt, Y. Nakanishi, and A. Gan. TCRP Report 141: A Methodology for Performance Measurement and Peer Comparison in the Public Transportation Industry. Transportation Research Board of the National Academies, Transit Cooperative Research Program, Washington, D.C., 2010.

16 6 to Success Homepage. The Heart of Texas Workforce Board, Inc. http://www.6tosuccess.com. Accessed August 2, 2010.

http://www.6tosuccess.com/

77

APPENDICES

79

APP

EN

DIX

A. A

LT

ER

NA

TIV

E R

UR

AL

CL

UST

ER

AN

AL

YSI

S D

AT

A D

ET

AIL

Tab

le A

1. R

ural

C1.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erEa

st T

exas

Cou

ncil

of G

over

nmen

ts

(Kilg

ore)

2.

654

0.36

6 0.

618

0.84

3 −0

.193

−0

.241

0.

351

00

10.

000

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5 −0

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1.

240

−0.4

02

−0.0

59

0.03

4 0

02

0.58

6 H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7 −0

.141

−0

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0.

966

−0.2

63

−0.3

32

0.13

4 0

02

0.67

8 G

olde

n C

resc

ent R

egio

nal P

lann

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Com

mis

sion

(Vic

toria

) 0.

056

0.05

6 −0

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0.

813

0.36

4 −0

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0.

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00

20.

683

Sout

h Pl

ains

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mun

ity A

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n A

ssoc

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n(L

evel

land

) −0

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0.

369

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0.26

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0.

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20.

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Cou

ntry

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nsit

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trict

(San

Sab

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00

20.

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p. (C

row

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00

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mun

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(S

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rock

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as R

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0.26

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iner

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mun

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nior

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ter R

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lic T

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stem

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031.

680

1.53

3 0.

754

Stan

dard

Dev

iatio

n 0.

245

0.43

80.

140

0.77

50.

819

1.42

4 2.

625

El P

aso

Cou

nty

−0.7

72 −0

.714

−0

.032

−1

.934

1.

444

2.13

6 1.

034

11

70.

000

82

Tab

le A

2. R

ural

C2

(Fiv

e C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSo

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.13

−0.5

60.

81−0

.16

−0.1

2−0

.56

−0.1

80

01

1.20

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.22

−0.4

70.

10−0

.07

−0.7

5−0

.69

−0.3

50

11

1.29

Hea

rt of

Tex

as C

ounc

il of

Gov

ernm

ents

(W

aco)

0.

11−0

.14

−0.2

80.

97−0

.26

−0.3

3 0.

130

01

1.32

Com

mun

ity S

ervi

ces,

Inc.

(Cor

sica

na)

−0.1

0−0

.58

0.99

−0.3

2−0

.12

−0.3

9 0.

080

11

1.33

Seni

or C

ente

r Res

ourc

es a

nd P

ublic

Tra

nsit

−0.4

8−0

.71

1.65

−0.0

7−0

.30

−0.3

8 0.

220

11

1.35

Texo

ma

Are

a Pa

ratra

nsit

Syst

em (T

APS

) (S

herm

an)

0.31

−0.1

3−0

.12

0.48

−0.7

9−0

.55

−0.3

20

11

1.40

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n (L

evel

land

) −0

.04

0.37

−0.7

80.

26−0

.47

0.04

−0

.13

00

11.

40C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

2−0

.42

−0.1

00.

940.

47−0

.06

−0.3

50

01

1.53

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

2−0

.01

−0.8

41.

36−0

.51

−0.3

7 −0

.02

00

11.

53G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

060.

06−0

.54

0.81

0.36

−0.0

5 0.

030

01

1.54

Kau

fman

Cou

nty

Seni

or C

itize

ns S

ervi

ce

(Ter

rell)

−0

.44

−0.7

01.

69−0

.74

−0.6

5−0

.61

−0.1

70

11

1.58

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.61

−0.1

1−0

.95

0.36

−0.0

90.

25

−0.0

50

01

1.58

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

000.

37−0

.76

1.24

−0.4

0−0

.06

0.03

00

11.

59B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.49

−0.3

2−0

.66

0.60

0.09

0.18

0.

400

01

1.61

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.45

−0.1

1−0

.03

0.87

0.22

−0.0

5 0.

320

01

1.66

Cle

burn

e (C

lebu

rne)

−0

.31

−0.7

33.

39−0

.80

−0.8

2−0

.73

−0.0

80

11

1.67

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

0.21

−0.6

71.

36−0

.58

0.31

−0

.25

00

11.

75A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

2−0

.04

−1.0

61.

73−0

.40

−0.1

0 −0

.12

00

11.

86C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

11.

07−1

.15

0.94

−0.4

0−0

.06

−0.3

80

01

1.91

83

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erTh

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

5−0

.61

0.26

1.30

−1.5

5−0

.79

−0.5

70

11

2.05

Gul

f Coa

st C

ente

r (G

alve

ston

) −0

.31

−0.6

20.

83−0

.32

1.44

−1.2

9 0.

000

11

2.41

Col

lin C

ount

y C

omm

ittee

on

Agi

ng

(McK

inne

y)

−0.6

1−0

.73

1.36

−1.6

0−1

.20

−1.4

5 −0

.82

01

12.

64Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.57

−0.7

21.

41−1

.66

−1.5

5−1

.03

−1.1

20

11

2.77

Fort

Ben

d C

ount

y −0

.73

−0.7

20.

36−1

.96

−1.3

1−1

.40

−0.8

30

11

2.95

Ave

rage

−0

.27

−0.2

70.

200.

23−0

.40

−0.4

2 −0

.19

St

anda

rd D

evia

tion

0.32

0.46

1.12

1.03

0.67

0.51

0.

37

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

462.

35−0

.99

0.26

−0.7

5−0

.26

−0.4

80

02

1.77

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

254.

59−1

.12

−0.0

10.

050.

22

0.22

10

21.

77A

vera

ge

0.36

3.47

−1.0

60.

13−0

.35

−0.0

2 −0

.13

St

anda

rd D

evia

tion

0.15

1.59

0.10

0.19

0.57

0.34

0.

50

C

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.43

−0.1

8−0

.74

−0.7

72.

212.

70

1.67

10

30.

94W

ebb

Cou

nty

Com

mun

ity A

ctio

n A

genc

y (L

ared

o)

−0.8

6−0

.41

−1.0

9−2

.24

2.49

3.00

1.

031

03

1.73

El P

aso

Cou

nty

−0.7

7−0

.71

−0.0

3−1

.93

1.44

2.14

1.

031

13

2.06

Com

mun

ity C

ounc

il of

Sou

thw

est T

exas

(U

vald

e)

−0.2

70.

55−0

.95

−0.2

21.

581.

54

4.29

10

32.

85A

vera

ge

−0.5

8−0

.19

−0.7

0−1

.29

1.93

2.34

2.

01

St

anda

rd D

evia

tion

0.28

0.54

0.47

0.95

0.50

0.64

1.

55

84

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erLo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

9−0

.49

0.22

−0.3

21.

161.

07

−0.0

71

04

0.91

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.35

−0.5

1−0

.02

−0.2

90.

920.

72

0.63

10

41.

35K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.77

−0.5

3−0

.82

−0.6

21.

76−0

.53

−0.3

81

04

1.38

Del

Rio

(Del

Rio

) −0

.68

−0.4

2−0

.81

−0.6

50.

360.

96

−2.8

31

04

2.33

Ave

rage

−0

.50

−0.4

9−0

.36

−0.4

71.

050.

56

−0.6

6

Stan

dard

Dev

iatio

n 0.

270.

040.

540.

190.

580.

74

1.51

Ala

mo

Are

a C

ounc

il of

Gov

ernm

ents

(S

an A

nton

io)

1.55

0.43

−0.0

20.

57−0

.58

−0.3

5 −0

.20

01

51.

27C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

0.07

0.64

−0.2

2−0

.86

−0.6

3 −0

.87

01

51.

48Ea

st T

exas

Cou

ncil

of G

over

nmen

ts

(Kilg

ore)

2.

650.

370.

620.

84−0

.19

−0.2

4 0.

350

05

1.75

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

1.26

0.25

0.11

0.09

−0.1

7 0.

120

15

1.96

Ave

rage

2.

530.

530.

370.

32−0

.38

−0.3

5 −0

.15

St

anda

rd D

evia

tion

1.18

0.51

0.32

0.47

0.41

0.20

0.

53

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

Clu

ster

1

23

45

1 4.

139

5.71

3.70

52.

973

2 4.

139

6.08

44.

715

4.18

63

5.71

6.08

4 3.

549

6.56

24

3.70

54.

715

3.54

9 5.

016

5 2.

973

4.18

66.

562

5.01

6

85

Tab

le A

3. R

ural

C2

(Six

Clu

ster

s).

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erLo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

85−0

.490

0.22

1−0

.317

1.16

51.

074

−0.0

661

01

0.91

4R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

01

1.34

8K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

01

1.38

2D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

01

2.33

3A

vera

ge

−0.4

97−0

.488

−0.3

58−0

.469

1.05

20.

556

−0.6

62St

anda

rd D

evia

tion

0.27

50.

045

0.53

90.

195

0.57

80.

742

1.50

9

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

251

4.59

3−1

.125

−0.0

110.

050

0.22

4 0.

217

10

20.

000

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

03

0.58

6H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

30.

678

Gol

den

Cre

scen

t Reg

iona

l Pla

nnin

g C

omm

issi

on (V

icto

ria)

0.05

60.

056

−0.5

400.

813

0.36

4−0

.049

0.

034

00

30.

683

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n(L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

00

30.

837

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

40.

207

−0.6

661.

362

−0.5

770.

305

−0.2

500

03

0.86

6R

ollin

g Pl

ains

Man

agem

ent C

orp.

(Cro

wel

l) −0

.420

−0.0

09−0

.843

1.36

2−0

.507

−0.3

73

−0.0

160

03

0.87

3C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

00

30.

958

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

31.

033

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

10

03

1.08

7C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

00

31.

104

Asp

erm

ont S

mal

l Bus

ines

s Dev

elop

men

t C

ente

r (A

sper

mon

t) −0

.716

−0.0

38−1

.062

1.72

9−0

.402

−0.1

00

−0.1

160

03

1.11

3C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

13−0

.114

−0.9

490.

355

−0.0

890.

255

−0.0

500

03

1.14

0

86

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSo

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.131

−0.5

650.

805

−0.1

64−0

.124

−0.5

65

−0.1

830

03

1.48

1Pa

nhan

dle

Com

mun

ity S

ervi

ces (

Am

arill

o)0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

03

2.41

6A

vera

ge

−0.1

520.

193

−0.5

720.

824

−0.2

03−0

.080

−0

.075

Stan

dard

Dev

iatio

n 0.

377

0.73

80.

522

0.51

80.

370

0.24

3 0.

254

Kau

fman

Cou

nty

Seni

or C

itize

ns S

ervi

ce

(Ter

rell)

−0

.441

−0.7

041.

689

−0.7

44−0

.646

−0.6

05

−0.1

660

14

0.40

4C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

40.

449

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

14

0.54

6C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

14

0.93

4Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it−0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

14

0.98

2Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

14

1.35

4C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

14

1.43

7Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

14

1.64

6Fo

rt B

end

Cou

nty

−0

.729

−0.7

220.

358

−1.9

65−1

.308

−1.4

05

−0.8

330

14

1.82

3Th

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

53−0

.606

0.26

31.

301

−1.5

52−0

.788

−0

.566

01

42.

042

Gul

f Coa

st C

ente

r (G

alve

ston

) −0

.313

−0.6

210.

828

−0.3

171.

444

−1.2

93

0.00

00

14

2.23

1A

vera

ge

−0.3

56−0

.611

1.08

2−0

.525

−0.6

91−0

.846

−0

.359

Stan

dard

Dev

iatio

n 0.

286

0.18

00.

993

0.97

30.

848

0.38

9 0.

427

87

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erA

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

15

1.27

1C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

10.

069

0.63

9−0

.225

−0.8

55−0

.626

−0

.867

01

51.

476

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

51.

753

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

51.

959

Ave

rage

2.

529

0.53

10.

370

0.32

5−0

.385

−0.3

47

−0.1

50St

anda

rd D

evia

tion

1.17

80.

511

0.31

80.

475

0.41

50.

200

0.52

8C

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.432

−0.1

82−0

.741

−0.7

742.

210

2.70

3 1.

668

10

60.

936

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

61.

729

El P

aso

Cou

nty

−0.7

72−0

.714

−0.0

32−1

.934

1.44

42.

136

1.03

41

16

2.06

1C

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

62.

852

Ave

rage

−0

.583

−0.1

87−0

.704

−1.2

931.

931

2.34

4 2.

005

Stan

dard

Dev

iatio

n 0.

279

0.54

00.

471

0.95

20.

499

0.64

4 1.

549

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

1

5.40

7 3.

605

4.34

3 5.

016

3.54

9 2

5.40

7

5.15

6 6.

372

5.57

8 6.

256

3 3.

605

5.15

6

2.75

6 3.

158

5.57

9 4

4.34

3 6.

372

2.75

6

3.32

1 6.

225

5 5.

016

5.57

8 3.

158

3.32

1

6.56

2 6

3.54

9 6.

256

5.57

9 6.

225

6.56

2

88

Tab

le A

4. R

ural

C2

(Sev

en C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

10.

000

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

12

0.47

4C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

20.

630

Kau

fman

Cou

nty

Seni

or C

itize

ns S

ervi

ce

(Ter

rell)

−0

.441

−0.7

041.

689

−0.7

44−0

.646

−0.6

05

−0.1

660

12

0.64

4C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

12

0.86

2Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

12

1.05

9Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

12

1.10

2C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

12

1.62

8Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

12

1.80

0Th

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

53−0

.606

0.26

31.

301

−1.5

52−0

.788

−0

.566

01

21.

995

Cap

ital A

rea

Rur

al T

rans

porta

tion

Syst

em

(CA

RTS

) (A

ustin

) 1.

771

0.06

90.

639

−0.2

25−0

.855

−0.6

26

−0.8

670

12

1.99

6Fo

rt B

end

Cou

nty

−0

.729

−0.7

220.

358

−1.9

65−1

.308

−1.4

05

−0.8

330

12

1.99

8A

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

12

2.23

5G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

22.

257

Ave

rage

−0

.046

−0.4

780.

963

−0.4

17−0

.694

−0.7

91

−0.3

86

St

anda

rd D

evia

tion

0.80

20.

370

0.96

10.

940

0.77

60.

383

0.41

8

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

03

0.58

6H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

30.

678

Gol

den

Cre

scen

t Reg

iona

l Pla

nnin

g C

omm

issi

on (V

icto

ria)

0.05

60.

056

−0.5

400.

813

0.36

4−0

.049

0.

034

00

30.

683

89

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSo

uth

Plai

ns C

omm

unity

Act

ion

Ass

ocia

tion

(Lev

ella

nd)

−0.0

380.

369

−0.7

800.

263

−0.4

720.

042

−0.1

330

03

0.83

7H

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

00

30.

866

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

30.

873

Col

orad

o V

alle

y Tr

ansi

t (C

olum

bus)

−0

.221

−0.4

19−0

.100

0.93

50.

468

−0.0

59

−0.3

500

03

0.95

8A

rk-T

ex C

ounc

il of

Gov

ernm

ents

(T

exar

kana

) 0.

449

−0.1

07−0

.033

0.87

40.

225

−0.0

49

0.31

70

03

1.03

3B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.486

−0.3

17−0

.665

0.59

90.

085

0.18

4 0.

401

00

31.

087

Con

cho

Val

ley

Cou

ncil

of G

over

nmen

ts

(San

Ang

elo)

−0

.609

1.06

5−1

.145

0.93

5−0

.402

−0.0

59

−0.3

830

03

1.10

4A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

31.

113

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.613

−0.1

14−0

.949

0.35

5−0

.089

0.25

5 −0

.050

00

31.

140

Sout

h Ea

st T

exas

Reg

iona

l Pla

nnin

g C

omm

issi

on (B

eaum

ont)

−0.1

31−0

.565

0.80

5−0

.164

−0.1

24−0

.565

−0

.183

00

31.

481

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

03

2.41

6A

vera

ge

−0.1

520.

193

−0.5

720.

824

−0.2

03−0

.080

−0

.075

Stan

dard

Dev

iatio

n 0.

377

0.73

80.

522

0.51

80.

370

0.24

3 0.

254

Lo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

85−0

.490

0.22

1−0

.317

1.16

51.

074

−0.0

661

04

0.91

4R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

04

1.34

8K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

04

1.38

2D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

04

2.33

3A

vera

ge

−0.4

97−0

.488

−0.3

58−0

.469

1.05

20.

556

−0.6

62

St

anda

rd D

evia

tion

0.27

50.

045

0.53

90.

195

0.57

80.

742

1.50

9

90

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erB

razo

s Tra

nsit

Dis

trict

(B

ryan

/Col

lege

Sta

tion)

4.

144

1.26

10.

246

0.11

10.

085

−0.1

70

0.11

70

15

1.39

6Ea

st T

exas

Cou

ncil

of G

over

nmen

ts

(Kilg

ore)

2.

654

0.36

60.

618

0.84

3−0

.193

−0.2

41

0.35

10

05

1.39

6A

vera

ge

3.39

90.

814

0.43

20.

477

−0.0

54−0

.206

0.

234

St

anda

rd D

evia

tion

1.05

40.

633

0.26

30.

518

0.19

70.

050

0.16

5

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

32−0

.182

−0.7

41−0

.774

2.21

02.

703

1.66

81

06

1.25

0El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

61.

617

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

61.

106

Ave

rage

−0

.688

−0.4

34−0

.622

−1.6

492.

047

2.61

2 1.

245

St

anda

rd D

evia

tion

0.22

60.

267

0.54

10.

773

0.54

10.

437

0.36

6

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

251

4.59

3−1

.125

−0.0

110.

050

0.22

4 0.

217

10

70.

000

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

7 1

6.

962

5.98

2 5.

200

7.00

7 3.

803

6.22

5 2

6.96

2

2.64

2 4.

307

4.06

8 6.

221

6.21

3 3

5.98

2 2.

642

3.

605

3.80

7 5.

761

5.15

6 4

5.20

0 4.

307

3.60

5

5.62

8 3.

376

5.40

7 5

7.00

7 4.

068

3.80

7 5.

628

7.

127

5.72

8 6

3.80

3 6.

221

5.76

1 3.

376

7.12

7

6.54

9 7

6.22

5 6.

213

5.15

6 5.

407

5.72

8 6.

549

91

Tab

le A

5. R

ural

C2

(Eig

ht C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erW

est T

exas

Opp

ortu

nitie

s, In

c. (L

ames

a)

0.25

14.

593

−1.1

25−0

.011

0.05

00.

224

0.21

71

01

0.00

0C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

20.

406

Col

lin C

ount

y C

omm

ittee

on

Agi

ng

(McK

inne

y)

−0.6

09−0

.729

1.35

8−1

.599

−1.2

03−1

.445

−0

.817

01

21.

241

Com

mun

ity S

ervi

ces,

Inc.

(Cor

sica

na)

−0.1

04−0

.577

0.98

6−0

.317

−0.1

24−0

.393

0.

084

01

21.

020

Fort

Ben

d C

ount

y

−0.7

29−0

.722

0.35

8−1

.965

−1.3

08−1

.405

−0

.833

01

21.

596

Gul

f Coa

st C

ente

r (G

alve

ston

) −0

.313

−0.6

210.

828

−0.3

171.

444

−1.2

93

0.00

00

12

2.17

1Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

12

1.13

7K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

20.

363

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

12

0.88

1Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

12

1.51

1A

vera

ge

−0.4

20−0

.665

1.30

6−0

.839

−0.5

84−0

.885

−0

.340

Stan

dard

Dev

iatio

n 0.

201

0.08

90.

955

0.72

90.

890

0.42

0 0.

471

A

rk-T

ex C

ounc

il of

Gov

ernm

ents

(T

exar

kana

) 0.

449

−0.1

07−0

.033

0.87

40.

225

−0.0

49

0.31

70

03

1.03

3A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

31.

113

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

10

03

1.08

7C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

13−0

.114

−0.9

490.

355

−0.0

890.

255

−0.0

500

03

1.14

0C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

00

31.

104

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

03

0.58

6C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

00

30.

958

92

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erG

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

03

0.68

3H

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

00

30.

866

Hea

rt of

Tex

as C

ounc

il of

Gov

ernm

ents

(W

aco)

0.

107

−0.1

41−0

.281

0.96

6−0

.263

−0.3

32

0.13

40

03

0.67

8Pa

nhan

dle

Com

mun

ity S

ervi

ces (

Am

arill

o)

0.46

12.

346

−0.9

860.

263

−0.7

51−0

.261

−0

.483

00

32.

416

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

30.

873

Sout

h Ea

st T

exas

Reg

iona

l Pla

nnin

g C

omm

issi

on (B

eaum

ont)

−0.1

31−0

.565

0.80

5−0

.164

−0.1

24−0

.565

−0

.183

00

31.

481

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n (L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

00

30.

837

Ave

rage

−0

.152

0.19

3−0

.572

0.82

4−0

.203

−0.0

80

−0.0

75

St

anda

rd D

evia

tion

0.37

70.

738

0.52

20.

518

0.37

00.

243

0.25

4

Com

mun

ity C

ounc

il of

Sou

thw

est T

exas

(U

vald

e)

−0.2

700.

553

−0.9

49−0

.225

1.58

31.

539

4.28

61

04

0.00

0B

razo

s Tra

nsit

Dis

trict

(B

ryan

/Col

lege

Sta

tion)

4.

144

1.26

10.

246

0.11

10.

085

−0.1

70

0.11

70

15

1.39

6Ea

st T

exas

Cou

ncil

of G

over

nmen

ts

(Kilg

ore)

2.

654

0.36

60.

618

0.84

3−0

.193

−0.2

41

0.35

10

05

1.39

6A

vera

ge

3.39

90.

814

0.43

20.

477

−0.0

54−0

.206

0.

234

St

anda

rd D

evia

tion

1.05

40.

633

0.26

30.

518

0.19

70.

050

0.16

5

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

32−0

.182

−0.7

41−0

.774

2.21

02.

703

1.66

81

06

1.25

0El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

61.

617

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

61.

106

Ave

rage

−0

.688

−0.4

34−0

.622

−1.6

492.

047

2.61

2 1.

245

St

anda

rd D

evia

tion

0.22

60.

267

0.54

10.

773

0.54

10.

437

0.36

6

93

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erA

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

17

1.18

1C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

10.

069

0.63

9−0

.225

−0.8

55−0

.626

−0

.867

01

71.

312

Texo

ma

Are

a Pa

ratra

nsit

Syst

em (T

APS

) (S

herm

an)

0.31

5−0

.126

−0.1

180.

477

−0.7

86−0

.555

−0

.316

01

70.

662

The

Tran

sit S

yste

m, I

nc. (

Gle

n R

ose)

−0

.453

−0.6

060.

263

1.30

1−1

.552

−0.7

88

−0.5

660

17

1.73

2A

vera

ge

0.79

5−0

.058

0.19

00.

531

−0.9

42−0

.580

−0

.487

Stan

dard

Dev

iatio

n 1.

050

0.43

10.

340

0.62

40.

423

0.18

0 0.

296

Del

Rio

(Del

Rio

) −0

.684

−0.4

25−0

.811

−0.6

520.

364

0.96

3 −2

.834

10

82.

333

Kle

berg

Cou

nty

Hum

an S

ervi

ces

(Kin

gsvi

lle)

−0.7

67−0

.528

−0.8

24−0

.622

1.75

7−0

.535

−0

.383

10

81.

382

Low

er R

io G

rand

e V

alle

y D

evel

opm

ent

Cou

ncil

(McA

llen)

−0

.185

−0.4

900.

221

−0.3

171.

165

1.07

4 −0

.066

10

80.

914

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.350

−0.5

08−0

.019

−0.2

860.

921

0.72

0 0.

634

10

81.

348

Ave

rage

−0

.497

−0.4

88−0

.358

−0.4

691.

052

0.55

6 −0

.662

Stan

dard

Dev

iatio

n 0.

275

0.04

50.

539

0.19

50.

578

0.74

2 1.

509

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

7 8

1

6.44

1 5.

156

6.22

5 5.

728

6.54

9 5.

905

5.40

7 2

6.44

1

2.93

1 6.

993

4.54

2 6.

137

1.99

4 4.

310

3 5.

156

2.93

1

5.98

2 3.

807

5.76

1 2.

473

3.60

5 4

6.22

5 6.

993

5.98

2

7.00

7 3.

803

7.08

8 5.

200

5 5.

728

4.54

2 3.

807

7.00

7

7.12

7 3.

179

5.62

8 6

6.54

9 6.

137

5.76

1 3.

803

7.12

7

6.61

7 3.

376

7 5.

905

1.99

4 2.

473

7.08

8 3.

179

6.61

7

4.60

9 8

5.40

7 4.

310

3.60

5 5.

200

5.62

8 3.

376

4.60

9

94

Tab

le A

6. R

ural

C3

(Fiv

e C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erPu

blic

Tra

nsit

Serv

ices

(Min

eral

Wel

ls)

−0.2

2−0

.47

0.10

−0.0

7−0

.75

−0.6

9 −0

.35

01

10.

69Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

31−0

.13

−0.1

20.

48−0

.79

−0.5

5 −0

.32

01

10.

77C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.10

−0.5

80.

99−0

.32

−0.1

2−0

.39

0.08

01

10.

77So

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.13

−0.5

60.

81−0

.16

−0.1

2−0

.56

−0.1

80

01

0.79

Seni

or C

ente

r Res

ourc

es a

nd P

ublic

Tra

nsit

−0.4

8−0

.71

1.65

−0.0

7−0

.30

−0.3

8 0.

220

11

0.90

Hea

rt of

Tex

as C

ounc

il of

Gov

ernm

ents

(W

aco)

0.

11−0

.14

−0.2

80.

97−0

.26

−0.3

3 0.

130

01

0.91

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n (L

evel

land

) −0

.04

0.37

−0.7

80.

26−0

.47

0.04

−0

.13

00

10.

99K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

4−0

.70

1.69

−0.7

4−0

.65

−0.6

1 −0

.17

01

11.

19G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

060.

06−0

.54

0.81

0.36

−0.0

5 0.

030

01

1.20

Col

orad

o V

alle

y Tr

ansi

t (C

olum

bus)

−0

.22

−0.4

2−0

.10

0.94

0.47

−0.0

6 −0

.35

00

11.

24R

ollin

g Pl

ains

Man

agem

ent C

orp.

(Cro

wel

l) −0

.42

−0.0

1−0

.84

1.36

−0.5

1−0

.37

−0.0

20

01

1.25

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

000.

37−0

.76

1.24

−0.4

0−0

.06

0.03

00

11.

27C

lebu

rne

(Cle

burn

e)

−0.3

1−0

.73

3.39

−0.8

0−0

.82

−0.7

3 −0

.08

01

11.

28C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

1−0

.11

−0.9

50.

36−0

.09

0.25

−0

.05

00

11.

32A

rk-T

ex C

ounc

il of

Gov

ernm

ents

(T

exar

kana

) 0.

45−0

.11

−0.0

30.

870.

22−0

.05

0.32

00

11.

33B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.49

−0.3

2−0

.66

0.60

0.09

0.18

0.

400

01

1.36

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

0.21

−0.6

71.

36−0

.58

0.31

−0

.25

00

11.

45A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

2−0

.04

−1.0

61.

73−0

.40

−0.1

0 −0

.12

00

11.

66C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

11.

07−1

.15

0.94

−0.4

0−0

.06

−0.3

80

01

1.66

95

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erTh

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

5−0

.61

0.26

1.30

−1.5

5−0

.79

−0.5

70

11

1.76

Ala

mo

Are

a C

ounc

il of

Gov

ernm

ents

(S

an A

nton

io)

1.55

0.43

−0.0

20.

57−0

.58

−0.3

5 −0

.20

01

11.

91C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

0.07

0.64

−0.2

2−0

.86

−0.6

3 −0

.87

01

12.

13G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

1−0

.62

0.83

−0.3

21.

44−1

.29

0.00

01

12.

19C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.61

−0.7

31.

36−1

.60

−1.2

0−1

.45

−0.8

20

11

2.42

Serv

ices

Pro

gram

for A

ging

Nee

ds (S

PAN

) (D

ento

n)

−0.5

7−0

.72

1.41

−1.6

6−1

.55

−1.0

3 −1

.12

01

12.

56Fo

rt B

end

Cou

nty

−0.7

3−0

.72

0.36

−1.9

6−1

.31

−1.4

0 −0

.83

01

12.

75A

vera

ge

−0.1

2−0

.23

0.21

0.22

−0.4

3−0

.43

−0.2

1

Stan

dard

Dev

iatio

n 0.

610.

461.

080.

990.

650.

49

0.38

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

3−0

.18

−0.7

4−0

.77

2.21

2.70

1.

671

02

0.79

El P

aso

Cou

nty

−0.7

7−0

.71

−0.0

3−1

.93

1.44

2.14

1.

031

12

1.40

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.86

−0.4

1−1

.09

−2.2

42.

493.

00

1.03

10

21.

65C

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.27

0.55

−0.9

5−0

.22

1.58

1.54

4.

291

02

2.81

Ave

rage

−0

.58

−0.1

9−0

.70

−1.2

91.

932.

34

2.01

Stan

dard

Dev

iatio

n 0.

280.

540.

470.

950.

500.

64

1.55

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

462.

35−0

.99

0.26

−0.7

5−0

.26

−0.4

80

03

1.33

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

254.

59−1

.12

−0.0

10.

050.

22

0.22

10

31.

33A

vera

ge

0.36

3.47

−1.0

60.

13−0

.35

−0.0

2 −0

.13

St

anda

rd D

evia

tion

0.15

1.59

0.10

0.19

0.57

0.34

0.

50

96

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erLo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

9−0

.49

0.22

−0.3

21.

161.

07

−0.0

71

04

0.91

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.35

−0.5

1−0

.02

−0.2

90.

920.

72

0.63

10

41.

35K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.77

−0.5

3−0

.82

−0.6

21.

76−0

.53

−0.3

81

04

1.38

Del

Rio

(Del

Rio

) −0

.68

−0.4

2−0

.81

−0.6

50.

360.

96

−2.8

31

04

2.33

Ave

rage

−0

.50

−0.4

9−0

.36

−0.4

71.

050.

56

−0.6

6

Stan

dard

Dev

iatio

n 0.

270.

040.

540.

190.

580.

74

1.51

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

1.26

0.25

0.11

0.09

−0.1

7 0.

120

15

0.96

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

0.37

0.62

0.84

−0.1

9−0

.24

0.35

00

50.

96A

vera

ge

3.40

0.81

0.43

0.48

−0.0

5−0

.21

0.23

Stan

dard

Dev

iatio

n 1.

050.

630.

260.

520.

200.

05

0.17

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

Clu

ster

1

23

45

1 5.

223.

801

2.74

53.

749

2 5.

22

5.95

13.

513

6.48

23

3.80

15.

951

4.57

4.19

74

2.74

53.

513

4.57

5.

027

5 3.

749

6.48

24.

197

5.02

7

97

Tab

le A

7. R

ural

C3

(Six

Clu

ster

s).

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erLo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

85−0

.490

0.22

1−0

.317

1.16

51.

074

−0.0

661

01

0.91

4R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

01

1.34

8K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

01

1.38

2D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

01

2.33

3A

vera

ge

−0.4

97−0

.488

−0.3

58−0

.469

1.05

20.

556

−0.6

62St

anda

rd D

evia

tion

0.27

50.

045

0.53

90.

195

0.57

80.

742

1.50

9C

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

20.

000

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.46

12.

346

−0.9

860.

263

−0.7

51−0

.261

−0

.483

00

31.

332

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

251

4.59

3−1

.125

−0.0

110.

050

0.22

4 0.

217

10

31.

332

Ave

rage

0.

356

3.46

9−1

.055

0.12

6−0

.350

−0.0

19

−0.1

33St

anda

rd D

evia

tion

0.14

91.

589

0.09

80.

194

0.56

60.

343

0.49

5

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

14

0.68

2C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

14

0.76

0So

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.131

−0.5

650.

805

−0.1

64−0

.124

−0.5

65

−0.1

830

04

0.76

4Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it−0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

14

0.79

8Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

14

0.87

0H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

40.

942

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n(L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

00

41.

055

Kau

fman

Cou

nty

Seni

or C

itize

ns S

ervi

ce

(Ter

rell)

−0

.441

−0.7

041.

689

−0.7

44−0

.646

−0.6

05

−0.1

660

14

1.14

3C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

00

41.

213

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

41.

219

98

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erG

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

04

1.22

7C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

41.

259

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.613

−0.1

14−0

.949

0.35

5−0

.089

0.25

5 −0

.050

00

41.

286

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

04

1.29

9B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.486

−0.3

17−0

.665

0.59

90.

085

0.18

4 0.

401

00

41.

318

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

41.

374

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

40.

207

−0.6

661.

362

−0.5

770.

305

−0.2

500

04

1.48

0A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

41.

617

Con

cho

Val

ley

Cou

ncil

of G

over

nmen

ts

(San

Ang

elo)

−0

.609

1.06

5−1

.145

0.93

5−0

.402

−0.0

59

−0.3

830

04

1.66

6Th

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

53−0

.606

0.26

31.

301

−1.5

52−0

.788

−0

.566

01

41.

740

Gul

f Coa

st C

ente

r (G

alve

ston

) −0

.313

−0.6

210.

828

−0.3

171.

444

−1.2

93

0.00

00

14

2.15

2C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

14

2.40

1Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

14

2.54

8Fo

rt B

end

Cou

nty

−0

.729

−0.7

220.

358

−1.9

65−1

.308

−1.4

05

−0.8

330

14

2.73

7A

vera

ge

−0.2

71−0

.265

0.20

30.

229

−0.4

04−0

.424

−0

.188

Stan

dard

Dev

iatio

n 0.

323

0.45

81.

121

1.02

50.

670

0.50

5 0.

366

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

50.

884

Ala

mo

Are

a C

ounc

il of

Gov

ernm

ents

(S

an A

nton

io)

1.54

70.

429

−0.0

230.

569

−0.5

77−0

.352

−0

.200

01

51.

166

Cap

ital A

rea

Rur

al T

rans

porta

tion

Syst

em

(CA

RTS

) (A

ustin

) 1.

771

0.06

90.

639

−0.2

25−0

.855

−0.6

26

−0.8

670

15

1.38

7B

razo

s Tra

nsit

Dis

trict

(B

ryan

/Col

lege

Sta

tion)

4.

144

1.26

10.

246

0.11

10.

085

−0.1

70

0.11

70

15

1.89

2A

vera

ge

2.52

90.

531

0.37

00.

325

−0.3

85−0

.347

−0

.150

Stan

dard

Dev

iatio

n 1.

178

0.51

10.

318

0.47

50.

415

0.20

0 0.

528

99

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erW

ebb

Cou

nty

Com

mun

ity A

ctio

n A

genc

y (L

ared

o)

−0.8

59−0

.407

−1.0

94−2

.240

2.48

92.

996

1.03

41

06

0.87

8El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

60.

896

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

32−0

.182

−0.7

41−0

.774

2.21

02.

703

1.66

81

06

1.05

3A

vera

ge

−0.6

88−0

.434

−0.6

22−1

.649

2.04

72.

612

1.24

5St

anda

rd D

evia

tion

0.22

60.

267

0.54

10.

773

0.54

10.

437

0.36

6

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

1

5.20

0 4.

570

2.73

5 4.

181

3.30

9 2

5.20

0

6.10

3 5.

804

6.33

6 3.

743

3 4.

570

6.10

3

3.86

4 3.

727

6.18

9 4

2.73

5 5.

804

3.86

4

2.91

4 5.

322

5 4.

181

6.33

6 3.

727

2.91

4

6.24

5 6

3.30

9 3.

743

6.18

9 5.

322

6.24

5

100

Tab

le A

8. R

ural

C3

(Sev

en C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

10.

000

Col

lin C

ount

y C

omm

ittee

on

Agi

ng

(McK

inne

y)

−0.6

09−0

.729

1.35

8−1

.599

−1.2

03−1

.445

−0

.817

01

20.

532

Serv

ices

Pro

gram

for A

ging

Nee

ds (S

PAN

) (D

ento

n)

−0.5

71−0

.722

1.40

6−1

.660

−1.5

52−1

.030

−1

.117

01

20.

766

Fort

Ben

d C

ount

y

−0.7

29−0

.722

0.35

8−1

.965

−1.3

08−1

.405

−0

.833

01

20.

874

Cle

burn

e (C

lebu

rne)

−0

.310

−0.7

263.

385

−0.8

05−0

.820

−0.7

27

−0.0

830

12

0.89

8K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

20.

993

Ave

rage

−0

.532

−0.7

211.

639

−1.3

54−1

.106

−1.0

42

−0.6

03

St

anda

rd D

evia

tion

0.16

10.

010

1.09

90.

548

0.36

80.

382

0.45

4

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

03

1.33

2W

est T

exas

Opp

ortu

nitie

s, In

c. (L

ames

a)

0.25

14.

593

−1.1

25−0

.011

0.05

00.

224

0.21

71

03

1.33

2A

vera

ge

0.35

63.

469

−1.0

550.

126

−0.3

50−0

.019

−0

.133

Stan

dard

Dev

iatio

n 0.

149

1.58

90.

098

0.19

40.

566

0.34

3 0.

495

Lo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

85−0

.490

0.22

1−0

.317

1.16

51.

074

−0.0

661

04

0.91

4R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

04

1.34

8K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

04

1.38

2D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

04

2.33

3A

vera

ge

−0.4

97−0

.488

−0.3

58−0

.469

1.05

20.

556

−0.6

62

St

anda

rd D

evia

tion

0.27

50.

045

0.53

90.

195

0.57

80.

742

1.50

9

101

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erEa

st T

exas

Cou

ncil

of G

over

nmen

ts

(Kilg

ore)

2.

654

0.36

60.

618

0.84

3−0

.193

−0.2

41

0.35

10

05

0.88

4A

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

15

1.16

6C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

10.

069

0.63

9−0

.225

−0.8

55−0

.626

−0

.867

01

51.

387

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

51.

892

Ave

rage

2.

529

0.53

10.

370

0.32

5−0

.385

−0.3

47

−0.1

50

St

anda

rd D

evia

tion

1.17

80.

511

0.31

80.

475

0.41

50.

200

0.52

8

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

60.

878

El P

aso

Cou

nty

−0.7

72−0

.714

−0.0

32−1

.934

1.44

42.

136

1.03

41

16

0.89

6C

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.432

−0.1

82−0

.741

−0.7

742.

210

2.70

3 1.

668

10

61.

053

Ave

rage

−0

.688

−0.4

34−0

.622

−1.6

492.

047

2.61

2 1.

245

St

anda

rd D

evia

tion

0.22

60.

267

0.54

10.

773

0.54

10.

437

0.36

6

Hea

rt of

Tex

as C

ounc

il of

Gov

ernm

ents

(W

aco)

0.

107

−0.1

41−0

.281

0.96

6−0

.263

−0.3

32

0.13

40

07

0.56

4G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

07

0.82

8C

entra

l Tex

as R

ural

Tra

nsit

Dis

trict

(C

olem

an)

0.00

20.

365

−0.7

581.

240

−0.4

02−0

.059

0.

034

00

70.

871

Col

orad

o V

alle

y Tr

ansi

t (C

olum

bus)

−0

.221

−0.4

19−0

.100

0.93

50.

468

−0.0

59

−0.3

500

07

0.87

4R

ollin

g Pl

ains

Man

agem

ent C

orp.

(Cro

wel

l) −0

.420

−0.0

09−0

.843

1.36

2−0

.507

−0.3

73

−0.0

160

07

0.87

5Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

17

0.96

8So

uth

Plai

ns C

omm

unity

Act

ion

Ass

ocia

tion

(Lev

ella

nd)

−0.0

380.

369

−0.7

800.

263

−0.4

720.

042

−0.1

330

07

0.99

6A

rk-T

ex C

ounc

il of

Gov

ernm

ents

(T

exar

kana

) 0.

449

−0.1

07−0

.033

0.87

40.

225

−0.0

49

0.31

70

07

1.01

8Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

17

1.08

1

102

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

17

1.08

4B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.486

−0.3

17−0

.665

0.59

90.

085

0.18

4 0.

401

00

71.

089

Sout

h Ea

st T

exas

Reg

iona

l Pla

nnin

g C

omm

issi

on (B

eaum

ont)

−0.1

31−0

.565

0.80

5−0

.164

−0.1

24−0

.565

−0

.183

00

71.

094

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

40.

207

−0.6

661.

362

−0.5

770.

305

−0.2

500

07

1.09

9Pu

blic

Tra

nsit

Serv

ices

(Min

eral

Wel

ls)

−0.2

18−0

.474

0.09

6−0

.072

−0.7

51−0

.686

−0

.350

01

71.

137

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.613

−0.1

14−0

.949

0.35

5−0

.089

0.25

5 −0

.050

00

71.

185

Asp

erm

ont S

mal

l Bus

ines

s Dev

elop

men

t C

ente

r (A

sper

mon

t) −0

.716

−0.0

38−1

.062

1.72

9−0

.402

−0.1

00

−0.1

160

07

1.22

9C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

00

71.

442

The

Tran

sit S

yste

m, I

nc. (

Gle

n R

ose)

−0

.453

−0.6

060.

263

1.30

1−1

.552

−0.7

88

−0.5

660

17

1.77

5G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

72.

236

Ave

rage

−0

.203

−0.1

45−0

.174

0.64

6−0

.219

−0.2

61

−0.0

79

St

anda

rd D

evia

tion

0.32

20.

442

0.78

30.

630

0.60

80.

399

0.25

4

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

7 1

6.

800

6.10

3 5.

200

6.33

6 3.

743

5.62

7 2

6.80

0

4.78

9 3.

471

3.87

1 5.

899

2.47

6 3

6.10

3 4.

789

4.

570

3.72

7 6.

189

3.75

6 4

5.20

0 3.

471

4.57

0

4.18

1 3.

309

2.74

9 5

6.33

6 3.

871

3.72

7 4.

181

6.

245

2.84

0 6

3.74

3 5.

899

6.18

9 3.

309

6.24

5

5.28

1 7

5.62

7 2.

476

3.75

6 2.

749

2.84

0 5.

281

103

Tab

le A

9. R

ural

C3

(Eig

ht C

lust

ers)

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erPa

nhan

dle

Com

mun

ity S

ervi

ces (

Am

arill

o)

0.46

12.

346

−0.9

860.

263

−0.7

51−0

.261

−0

.483

00

11.

332

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

251

4.59

3−1

.125

−0.0

110.

050

0.22

4 0.

217

10

11.

332

Ave

rage

0.

356

3.46

9−1

.055

0.12

6−0

.350

−0.0

19

−0.1

33

St

anda

rd D

evia

tion

0.14

91.

589

0.09

80.

194

0.56

60.

343

0.49

5

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

10

02

0.88

3C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

12

0.90

2G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

02

0.97

5C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

13−0

.114

−0.9

490.

355

−0.0

890.

255

−0.0

500

02

0.98

2C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

00

21.

005

Seni

or C

ente

r Res

ourc

es a

nd P

ublic

Tra

nsit

−0.4

81−0

.710

1.64

8−0

.072

−0.2

98−0

.383

0.

217

01

21.

207

Sout

h Ea

st T

exas

Reg

iona

l Pla

nnin

g C

omm

issi

on (B

eaum

ont)

−0.1

31−0

.565

0.80

5−0

.164

−0.1

24−0

.565

−0

.183

00

21.

245

Gul

f Coa

st C

ente

r (G

alve

ston

) −0

.313

−0.6

210.

828

−0.3

171.

444

−1.2

93

0.00

00

12

1.60

6R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

02

1.75

0K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

02

2.02

8A

vera

ge

−0.3

41−0

.430

0.11

70.

093

0.44

1−0

.212

0.

040

St

anda

rd D

evia

tion

0.25

10.

240

0.89

50.

541

0.71

20.

553

0.31

7

Del

Rio

(Del

Rio

) −0

.684

−0.4

25−0

.811

−0.6

520.

364

0.96

3 −2

.834

10

31.

475

Low

er R

io G

rand

e V

alle

y D

evel

opm

ent

Cou

ncil

(McA

llen)

−0

.185

−0.4

900.

221

−0.3

171.

165

1.07

4 −0

.066

10

31.

475

Ave

rage

−0

.435

−0.4

57−0

.295

−0.4

840.

764

1.01

8 −1

.450

Stan

dard

Dev

iatio

n 0.

353

0.04

60.

730

0.23

70.

566

0.07

9 1.

957

104

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

40.

000

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

50.

964

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

50.

964

Ave

rage

3.

399

0.81

40.

432

0.47

7−0

.054

−0.2

06

0.23

4

Stan

dard

Dev

iatio

n 1.

054

0.63

30.

263

0.51

80.

197

0.05

0 0.

165

W

ebb

Cou

nty

Com

mun

ity A

ctio

n A

genc

y (L

ared

o)

−0.8

59−0

.407

−1.0

94−2

.240

2.48

92.

996

1.03

41

06

0.87

8El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

60.

896

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

32−0

.182

−0.7

41−0

.774

2.21

02.

703

1.66

81

06

1.05

3A

vera

ge

−0.6

88−0

.434

−0.6

22−1

.649

2.04

72.

612

1.24

5

Stan

dard

Dev

iatio

n 0.

226

0.26

70.

541

0.77

30.

541

0.43

7 0.

366

C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

17

0.53

2Se

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

17

0.76

6Fo

rt B

end

Cou

nty

−0

.729

−0.7

220.

358

−1.9

65−1

.308

−1.4

05

−0.8

330

17

0.87

4C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

70.

898

Kau

fman

Cou

nty

Seni

or C

itize

ns S

ervi

ce

(Ter

rell)

−0

.441

−0.7

041.

689

−0.7

44−0

.646

−0.6

05

−0.1

660

17

0.99

3A

vera

ge

−0.5

32−0

.721

1.63

9−1

.354

−1.1

06−1

.042

−0

.603

Stan

dard

Dev

iatio

n 0.

161

0.01

01.

099

0.54

80.

368

0.38

2 0.

454

H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

80.

566

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

08

0.63

9Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

18

0.68

5

105

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erH

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

00

80.

825

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

80.

842

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n(L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

00

81.

008

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

81.

097

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

18

1.24

9A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

81.

274

Con

cho

Val

ley

Cou

ncil

of G

over

nmen

ts

(San

Ang

elo)

−0

.609

1.06

5−1

.145

0.93

5−0

.402

−0.0

59

−0.3

830

08

1.37

6Th

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

53−0

.606

0.26

31.

301

−1.5

52−0

.788

−0

.566

01

81.

586

Ala

mo

Are

a C

ounc

il of

Gov

ernm

ents

(S

an A

nton

io)

1.54

70.

429

−0.0

230.

569

−0.5

77−0

.352

−0

.200

01

81.

591

Cap

ital A

rea

Rur

al T

rans

porta

tion

Syst

em

(CA

RTS

) (A

ustin

) 1.

771

0.06

90.

639

−0.2

25−0

.855

−0.6

26

−0.8

670

18

2.11

2A

vera

ge

0.13

60.

077

−0.3

620.

829

−0.5

63−0

.279

−0

.209

Stan

dard

Dev

iatio

n 0.

759

0.42

80.

551

0.59

40.

403

0.32

5 0.

307

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

3 4

5 6

7 8

1

4.05

1 4.

753

6.10

3 4.

197

6.18

9 4.

789

3.55

2 2

4.05

1

2.60

0 5.

148

4.07

7 4.

477

2.46

4 1.

540

3 4.

753

2.60

0

5.91

4 5.

300

3.63

8 3.

763

3.44

1 4

6.10

3 5.

148

5.91

4

6.53

3 3.

743

6.80

0 5.

893

5 4.

197

4.07

7 5.

300

6.53

3

6.73

0 4.

870

3.44

4 6

6.18

9 4.

477

3.63

8 3.

743

6.73

0

5.89

9 5.

641

7 4.

789

2.46

4 3.

763

6.80

0 4.

870

5.89

9

2.62

5 8

3.55

2 1.

540

3.44

1 5.

893

3.44

4 5.

641

2.62

5

106

Tab

le A

10. R

ural

Fiv

e C

lust

ers w

ithou

t the

Var

iabl

es o

f Per

cent

of H

Hs w

ithou

t Aut

os a

nd P

erce

nt b

elow

Pov

erty

Lev

el.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.432

−0.1

82−0

.741

−0.7

742.

210

2.70

3 1.

668

10

10.

829

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.350

−0.5

08−0

.019

−0.2

860.

921

0.72

0 0.

634

10

11.

168

Low

er R

io G

rand

e V

alle

y D

evel

opm

ent

Cou

ncil

(McA

llen)

−0

.185

−0.4

900.

221

−0.3

171.

165

1.07

4 −0

.066

10

11.

533

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

11.

637

Kle

berg

Cou

nty

Hum

an S

ervi

ces

(Kin

gsvi

lle)

−0.7

67−0

.528

−0.8

24−0

.622

1.75

7−0

.535

−0

.383

10

11.

644

El P

aso

Cou

nty

−0.7

72−0

.714

−0.0

32−1

.934

1.44

42.

136

1.03

41

11

2.07

7C

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

13.

330

Ave

rage

−0

.519

−0.3

25−0

.491

−0.9

141.

653

1.51

9 1.

172

St

anda

rd D

evia

tion

0.27

40.

419

0.53

00.

830

0.55

41.

226

1.53

9

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

02

1.70

9W

est T

exas

Opp

ortu

nitie

s, In

c. (L

ames

a)

0.25

14.

593

−1.1

25−0

.011

0.05

00.

224

0.21

71

02

1.70

9A

vera

ge

0.35

63.

469

−1.0

550.

126

−0.3

50−0

.019

−0

.133

Stan

dard

Dev

iatio

n 0.

149

1.58

90.

098

0.19

40.

566

0.34

3 0.

495

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

13

1.14

0So

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.131

−0.5

650.

805

−0.1

64−0

.124

−0.5

65

−0.1

830

03

1.17

4So

uth

Plai

ns C

omm

unity

Act

ion

Ass

ocia

tion

(Lev

ella

nd)

−0.0

380.

369

−0.7

800.

263

−0.4

720.

042

−0.1

330

03

1.17

9Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

13

1.19

2C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

13−0

.114

−0.9

490.

355

−0.0

890.

255

−0.0

500

03

1.19

6C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

13

1.27

2Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

13

1.29

4

107

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erH

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

31.

333

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

10

03

1.39

3G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

03

1.47

1K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

31.

529

Col

orad

o V

alle

y Tr

ansi

t (C

olum

bus)

−0

.221

−0.4

19−0

.100

0.93

50.

468

−0.0

59

−0.3

500

03

1.54

7C

entra

l Tex

as R

ural

Tra

nsit

Dis

trict

(C

olem

an)

0.00

20.

365

−0.7

581.

240

−0.4

02−0

.059

0.

034

00

31.

574

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

31.

578

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

31.

579

Cle

burn

e (C

lebu

rne)

−0

.310

−0.7

263.

385

−0.8

05−0

.820

−0.7

27

−0.0

830

13

1.59

3H

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

00

31.

596

Con

cho

Val

ley

Cou

ncil

of G

over

nmen

ts

(San

Ang

elo)

−0

.609

1.06

5−1

.145

0.93

5−0

.402

−0.0

59

−0.3

830

03

1.85

9A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

31.

918

The

Tran

sit S

yste

m, I

nc. (

Gle

n R

ose)

−0

.453

−0.6

060.

263

1.30

1−1

.552

−0.7

88

−0.5

660

13

1.95

4A

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

13

2.09

2G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

32.

248

Cap

ital A

rea

Rur

al T

rans

porta

tion

Syst

em

(CA

RTS

) (A

ustin

) 1.

771

0.06

90.

639

−0.2

25−0

.855

−0.6

26

−0.8

670

13

2.34

6C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

13

2.40

8

108

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSe

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

13

2.66

4Fo

rt B

end

Cou

nty

−0.7

29−0

.722

0.35

8−1

.965

−1.3

08−1

.405

−0

.833

01

32.

754

Ave

rage

−0

.123

−0.2

260.

211

0.22

5−0

.428

−0.4

29

−0.2

14

St

anda

rd D

evia

tion

0.61

00.

464

1.07

90.

990

0.64

90.

486

0.37

5

Del

Rio

(Del

Rio

) −0

.684

−0.4

25−0

.811

−0.6

520.

364

0.96

3 −2

.834

10

40.

000

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

51.

394

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

51.

394

Ave

rage

2.

038

0.40

10.

017

0.10

10.

085

0.18

4 −0

.789

Stan

dard

Dev

iatio

n 2.

472

0.84

40.

741

0.74

80.

279

0.67

6 1.

775

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

12

34

51

4.

836

3.69

14.

231

5.31

92

4.83

6

4.03

45.

099

4.36

23

3.69

14.

034

3.

875

3.72

74

4.23

15.

099

3.87

5

5.95

65

5.31

94.

362

3.72

75.

956

109

Tab

le A

11. R

ural

Six

Clu

ster

s with

out t

he V

aria

bles

of P

erce

nt o

f HH

s with

out A

utos

and

Per

cent

bel

ow P

over

ty L

evel

.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSo

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.131

−0.5

650.

805

−0.1

64−0

.124

−0.5

65

−0.1

830

01

1.15

8C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

00

11.

197

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

11

1.21

3G

olde

n C

resc

ent R

egio

nal P

lann

ing

Com

mis

sion

(Vic

toria

) 0.

056

0.05

6−0

.540

0.81

30.

364

−0.0

49

0.03

40

01

1.27

8G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

11.

288

Com

mun

ity S

ervi

ces,

Inc.

(Cor

sica

na)

−0.1

04−0

.577

0.98

6−0

.317

−0.1

24−0

.393

0.

084

01

11.

301

Hea

rt of

Tex

as C

ounc

il of

Gov

ernm

ents

(W

aco)

0.

107

−0.1

41−0

.281

0.96

6−0

.263

−0.3

32

0.13

40

01

1.31

0So

uth

Plai

ns C

omm

unity

Act

ion

Ass

ocia

tion

(Lev

ella

nd)

−0.0

380.

369

−0.7

800.

263

−0.4

720.

042

−0.1

330

01

1.32

2Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

11

1.33

8Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

11

1.34

9C

apro

ck C

omm

unity

Act

ion

Ass

ocia

tion

(Cro

sbyt

on)

−0.6

13−0

.114

−0.9

490.

355

−0.0

890.

255

−0.0

500

01

1.39

6B

ee C

omm

unity

Act

ion

Age

ncy

(Bee

ville

) −0

.486

−0.3

17−0

.665

0.59

90.

085

0.18

4 0.

401

00

11.

408

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

11.

486

Rol

ling

Plai

ns M

anag

emen

t Cor

p. (C

row

ell)

−0.4

20−0

.009

−0.8

431.

362

−0.5

07−0

.373

−0

.016

00

11.

526

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

01

1.55

2K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

11.

553

Hill

Cou

ntry

Tra

nsit

Dis

trict

(San

Sab

a)

0.02

40.

207

−0.6

661.

362

−0.5

770.

305

−0.2

500

01

1.57

7C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

11.

586

The

Tran

sit S

yste

m, I

nc. (

Gle

n R

ose)

−0

.453

−0.6

060.

263

1.30

1−1

.552

−0.7

88

−0.5

660

11

1.66

5A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

11.

834

110

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

00

11.

871

Col

lin C

ount

y C

omm

ittee

on

Agi

ng

(McK

inne

y)

−0.6

09−0

.729

1.35

8−1

.599

−1.2

03−1

.445

−0

.817

01

12.

297

Serv

ices

Pro

gram

for A

ging

Nee

ds (S

PAN

) (D

ento

n)

−0.5

71−0

.722

1.40

6−1

.660

−1.5

52−1

.030

−1

.117

01

12.

450

Fort

Ben

d C

ount

y −0

.729

−0.7

220.

358

−1.9

65−1

.308

−1.4

05

−0.8

330

11

2.62

8A

vera

ge

−0.2

68−0

.031

−0.1

52−0

.087

−0.4

14−0

.306

−0

.094

Stan

dard

Dev

iatio

n 0.

392

0.87

90.

906

0.91

40.

587

0.45

2 0.

273

Com

mun

ity C

ounc

il of

Sou

thw

est T

exas

(U

vald

e)

−0.2

700.

553

−0.9

49−0

.225

1.58

31.

539

4.28

61

02

0.00

0

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

03

1.70

3W

est T

exas

Opp

ortu

nitie

s, In

c. (L

ames

a)

0.25

14.

593

−1.1

25−0

.011

0.05

00.

224

0.21

71

03

1.70

3A

vera

ge

0.35

63.

469

−1.0

550.

126

−0.3

50−0

.019

−0

.133

Stan

dard

Dev

iatio

n 0.

149

1.58

90.

098

0.19

40.

566

0.34

3 0.

495

Com

mun

ity A

ct. C

ounc

il of

Sou

th T

exas

(R

io G

rand

e C

ity)

−0.4

32−0

.182

−0.7

41−0

.774

2.21

02.

703

1.66

81

04

1.01

2W

ebb

Cou

nty

Com

mun

ity A

ctio

n A

genc

y (L

ared

o)

−0.8

59−0

.407

−1.0

94−2

.240

2.48

92.

996

1.03

41

04

1.13

7R

ural

Eco

nom

ic A

ssis

tanc

e Le

ague

, Inc

. (R

EAL)

(Alic

e)

−0.3

50−0

.508

−0.0

19−0

.286

0.92

10.

720

0.63

41

04

1.46

3El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

41.

682

Ave

rage

−0

.603

−0.4

53−0

.472

−1.3

091.

766

2.13

9 1.

093

Stan

dard

Dev

iatio

n 0.

250

0.22

10.

535

0.92

90.

716

1.01

1 0.

427

111

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erA

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

15

1.25

6C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

10.

069

0.63

9−0

.225

−0.8

55−0

.626

−0

.867

01

51.

371

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

51.

739

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

51.

893

Ave

rage

2.

529

0.53

10.

370

0.32

5−0

.385

−0.3

47

−0.1

50

St

anda

rd D

evia

tion

1.17

80.

511

0.31

80.

475

0.41

50.

200

0.52

8

K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

06

0.80

4Lo

wer

Rio

Gra

nde

Val

ley

Dev

elop

men

t C

ounc

il (M

cAlle

n)

−0.1

85−0

.490

0.22

1−0

.317

1.16

51.

074

−0.0

661

06

1.13

1D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

06

1.75

2A

vera

ge

−0.5

45−0

.481

−0.4

71−0

.530

1.09

50.

501

−1.0

94

St

anda

rd D

evia

tion

0.31

50.

052

0.60

00.

186

0.69

90.

898

1.51

5

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

12

34

56

1

5.66

84.

119

3.96

72.

972

3.40

6 2

5.66

8

5.69

53.

577

6.35

65.

501

3 4.

119

5.69

5

4.97

54.

173

4.56

6 4

3.96

73.

577

4.97

5

5.22

72.

428

5 2.

972

6.35

64.

173

5.22

7

4.85

1 6

3.40

65.

501

4.56

62.

428

4.85

1

112

Tab

le A

12. R

ural

Sev

en C

lust

ers w

ithou

t the

Var

iabl

es o

f Per

cent

of H

Hs w

ithou

t Aut

os a

nd P

erce

nt b

elow

Pov

erty

Lev

el.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

omm

unity

Cou

ncil

of S

outh

wes

t Tex

as

(Uva

lde)

−0

.270

0.55

3−0

.949

−0.2

251.

583

1.53

9 4.

286

10

10.

00K

lebe

rg C

ount

y H

uman

Ser

vice

s (K

ings

ville

) −0

.767

−0.5

28−0

.824

−0.6

221.

757

−0.5

35

−0.3

831

02

0.80

Low

er R

io G

rand

e V

alle

y D

evel

opm

ent

Cou

ncil

(McA

llen)

−0

.185

−0.4

900.

221

−0.3

171.

165

1.07

4 −0

.066

10

21.

13D

el R

io (D

el R

io)

−0.6

84−0

.425

−0.8

11−0

.652

0.36

40.

963

−2.8

341

02

1.75

Ave

rage

−0

.545

−0.4

81−0

.471

−0.5

301.

095

0.50

1 −1

.094

Stan

dard

Dev

iatio

n 0.

315

0.05

20.

600

0.18

60.

699

0.89

8 1.

515

Gol

den

Cre

scen

t Reg

iona

l Pla

nnin

g C

omm

issi

on (V

icto

ria)

0.05

60.

056

−0.5

400.

813

0.36

4−0

.049

0.

034

10

30.

38C

entra

l Tex

as R

ural

Tra

nsit

Dis

trict

(C

olem

an)

0.00

20.

365

−0.7

581.

240

−0.4

02−0

.059

0.

034

10

30.

55H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

10

30.

63H

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

10

30.

68C

olor

ado

Val

ley

Tran

sit (

Col

umbu

s)

−0.2

21−0

.419

−0.1

000.

935

0.46

8−0

.059

−0

.350

10

30.

68R

ollin

g Pl

ains

Man

agem

ent C

orp.

(Cro

wel

l)−0

.420

−0.0

09−0

.843

1.36

2−0

.507

−0.3

73

−0.0

161

03

0.77

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n(L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

10

30.

78A

rk-T

ex C

ounc

il of

Gov

ernm

ents

(T

exar

kana

) 0.

449

−0.1

07−0

.033

0.87

40.

225

−0.0

49

0.31

71

03

0.94

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

11

03

1.01

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.613

−0.1

14−0

.949

0.35

5−0

.089

0.25

5 −0

.050

10

31.

08C

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

10

31.

09A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

10

31.

10

113

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSo

uth

East

Tex

as R

egio

nal P

lann

ing

Com

mis

sion

(Bea

umon

t) −0

.131

−0.5

650.

805

−0.1

64−0

.124

−0.5

65

−0.1

831

03

1.40

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

831

03

2.35

Ave

rage

−0

.152

0.19

3−0

.572

0.82

4−0

.203

−0.0

80

−0.0

75

St

anda

rd D

evia

tion

0.37

70.

738

0.52

20.

518

0.37

00.

243

0.25

4

C

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.432

−0.1

82−0

.741

−0.7

742.

210

2.70

3 1.

668

10

41.

01W

ebb

Cou

nty

Com

mun

ity A

ctio

n A

genc

y (L

ared

o)

−0.8

59−0

.407

−1.0

94−2

.240

2.48

92.

996

1.03

41

04

1.14

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.350

−0.5

08−0

.019

−0.2

860.

921

0.72

0 0.

634

10

41.

46El

Pas

o C

ount

y −0

.772

−0.7

14−0

.032

−1.9

341.

444

2.13

6 1.

034

11

41.

68A

vera

ge

−0.6

03−0

.453

−0.4

72−1

.309

1.76

62.

139

1.09

3

St

anda

rd D

evia

tion

0.25

00.

221

0.53

50.

929

0.71

61.

011

0.42

7

A

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

15

1.26

Cap

ital A

rea

Rur

al T

rans

porta

tion

Syst

em

(CA

RTS

) (A

ustin

) 1.

771

0.06

90.

639

−0.2

25−0

.855

−0.6

26

−0.8

670

15

1.37

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

01

51.

74B

razo

s Tra

nsit

Dis

trict

B

ryan

/Col

lege

Sta

tion)

4.

144

1.26

10.

246

0.11

10.

085

−0.1

70

0.11

70

15

1.89

Ave

rage

2.

529

0.53

10.

370

0.32

5−0

.385

−0.3

47

−0.1

50

St

anda

rd D

evia

tion

1.17

80.

511

0.31

80.

475

0.41

50.

200

0.52

8

K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

60.

32C

lebu

rne

(Cle

burn

e)

−0.3

10−0

.726

3.38

5−0

.805

−0.8

20−0

.727

−0

.083

01

60.

41G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

60.

47Pu

blic

Tra

nsit

Serv

ices

(Min

eral

Wel

ls)

−0.2

18−0

.474

0.09

6−0

.072

−0.7

51−0

.686

−0

.350

01

60.

52C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

16

0.59

114

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSe

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

16

0.77

Col

lin C

ount

y C

omm

ittee

on

Agi

ng

(McK

inne

y)

−0.6

09−0

.729

1.35

8−1

.599

−1.2

03−1

.445

−0

.817

01

61.

20Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

16

1.32

Serv

ices

Pro

gram

for A

ging

Nee

ds (S

PAN

) (D

ento

n)

−0.5

71−0

.722

1.40

6−1

.660

−1.5

52−1

.030

−1

.117

01

61.

39Fo

rt B

end

Cou

nty

−0.7

29−0

.722

0.35

8−1

.965

−1.3

08−1

.405

−0

.833

01

61.

62Th

e Tr

ansi

t Sys

tem

, Inc

. (G

len

Ros

e)

−0.4

53−0

.606

0.26

31.

301

−1.5

52−0

.788

−0

.566

01

61.

85A

vera

ge

−0.3

56−0

.611

1.08

2−0

.525

−0.6

91−0

.846

−0

.359

Stan

dard

Dev

iatio

n 0.

286

0.18

00.

993

0.97

30.

848

0.38

9 0.

427

Wes

t Tex

as O

ppor

tuni

ties,

Inc.

(Lam

esa)

0.

251

4.59

3−1

.125

−0.0

110.

050

0.22

4 0.

217

10

70.

00

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

34

56

7 1

5.

501

5.47

53.

577

6.35

66.

187

5.88

9 2

5.50

1

3.48

22.

428

4.85

13.

820

5.40

8 3

5.47

5 3.

482

4.

178

3.14

22.

602

5.14

1 4

3.57

7 2.

428

4.17

8

5.22

74.

169

5.56

4 5

6.35

6 4.

851

3.14

25.

227

3.

269

5.53

2 6

6.18

7 3.

822.

602

4.16

9 3.

269

6.

238

7 5.

889

5.40

85.

141

5.56

4 5.

532

6.23

8

115

Tab

le A

13. R

ural

Eig

ht C

lust

ers w

ithou

t the

Var

iabl

es o

f Per

cent

of H

Hs w

ithou

t Aut

os a

nd P

erce

nt b

elow

Pov

erty

Lev

el.

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erW

est T

exas

Opp

ortu

nitie

s, In

c. (L

ames

a)

0.25

14.

593

−1.1

25−0

.011

0.05

00.

224

0.21

71

01

0.00

0C

omm

unity

Act

. Cou

ncil

of S

outh

Tex

as

(Rio

Gra

nde

City

) −0

.432

−0.1

82−0

.741

−0.7

742.

210

2.70

3 1.

668

10

21.

012

Web

b C

ount

y C

omm

unity

Act

ion

Age

ncy

(Lar

edo)

−0

.859

−0.4

07−1

.094

−2.2

402.

489

2.99

6 1.

034

10

21.

137

Rur

al E

cono

mic

Ass

ista

nce

Leag

ue, I

nc.

(REA

L) (A

lice)

−0

.350

−0.5

08−0

.019

−0.2

860.

921

0.72

0 0.

634

10

21.

463

El P

aso

Cou

nty

−0.7

72−0

.714

−0.0

32−1

.934

1.44

42.

136

1.03

41

12

1.68

2

Ave

rage

−0

.603

−0.4

53−0

.472

−1.3

091.

766

2.13

9 1.

093

Stan

dard

Dev

iatio

n 0.

250

0.22

10.

535

0.92

90.

716

1.01

1 0.

427

Gol

den

Cre

scen

t Reg

iona

l Pla

nnin

g C

omm

issi

on (V

icto

ria)

0.05

60.

056

−0.5

400.

813

0.36

4−0

.049

0.

034

00

30.

387

Col

orad

o V

alle

y Tr

ansi

t (C

olum

bus)

−0

.221

−0.4

19−0

.100

0.93

50.

468

−0.0

59

−0.3

500

03

0.54

6H

eart

of T

exas

Cou

ncil

of G

over

nmen

ts

(Wac

o)

0.10

7−0

.141

−0.2

810.

966

−0.2

63−0

.332

0.

134

00

30.

554

Cen

tral T

exas

Rur

al T

rans

it D

istri

ct

(Col

eman

) 0.

002

0.36

5−0

.758

1.24

0−0

.402

−0.0

59

0.03

40

03

0.60

0R

ollin

g Pl

ains

Man

agem

ent C

orp.

(Cro

wel

l)−0

.420

−0.0

09−0

.843

1.36

2−0

.507

−0.3

73

−0.0

160

03

0.68

4H

ill C

ount

ry T

rans

it D

istri

ct (S

an S

aba)

0.

024

0.20

7−0

.666

1.36

2−0

.577

0.30

5 −0

.250

00

30.

689

Sout

h Pl

ains

Com

mun

ity A

ctio

n A

ssoc

iatio

n(L

evel

land

) −0

.038

0.36

9−0

.780

0.26

3−0

.472

0.04

2 −0

.133

00

30.

881

Ark

-Tex

Cou

ncil

of G

over

nmen

ts

(Tex

arka

na)

0.44

9−0

.107

−0.0

330.

874

0.22

5−0

.049

0.

317

00

30.

914

Bee

Com

mun

ity A

ctio

n A

genc

y (B

eevi

lle)

−0.4

86−0

.317

−0.6

650.

599

0.08

50.

184

0.40

10

03

0.92

8A

sper

mon

t Sm

all B

usin

ess D

evel

opm

ent

Cen

ter (

Asp

erm

ont)

−0.7

16−0

.038

−1.0

621.

729

−0.4

02−0

.100

−0

.116

00

31.

012

Cap

rock

Com

mun

ity A

ctio

n A

ssoc

iatio

n (C

rosb

yton

) −0

.613

−0.1

14−0

.949

0.35

5−0

.089

0.25

5 −0

.050

00

31.

056

116

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erC

onch

o V

alle

y C

ounc

il of

Gov

ernm

ents

(S

an A

ngel

o)

−0.6

091.

065

−1.1

450.

935

−0.4

02−0

.059

−0

.383

00

31.

213

Sout

h Ea

st T

exas

Reg

iona

l Pla

nnin

g C

omm

issi

on (B

eaum

ont)

−0.1

31−0

.565

0.80

5−0

.164

−0.1

24−0

.565

−0

.183

00

31.

348

Ave

rage

−0

.200

0.02

7−0

.540

0.86

7−0

.161

−0.0

66

−0.0

43

Stan

dard

Dev

iatio

n 0.

346

0.41

60.

529

0.51

30.

349

0.24

7 0.

234

Com

mun

ity C

ounc

il of

Sou

thw

est T

exas

(U

vald

e)

−0.2

700.

553

−0.9

49−0

.225

1.58

31.

539

4.28

61

04

0.00

0A

lam

o A

rea

Cou

ncil

of G

over

nmen

ts

(San

Ant

onio

) 1.

547

0.42

9−0

.023

0.56

9−0

.577

−0.3

52

−0.2

000

15

1.25

6C

apita

l Are

a R

ural

Tra

nspo

rtatio

n Sy

stem

(C

AR

TS) (

Aus

tin)

1.77

10.

069

0.63

9−0

.225

−0.8

55−0

.626

−0

.867

01

51.

371

East

Tex

as C

ounc

il of

Gov

ernm

ents

(K

ilgor

e)

2.65

40.

366

0.61

80.

843

−0.1

93−0

.241

0.

351

00

51.

739

Bra

zos T

rans

it D

istri

ct

(Bry

an/C

olle

ge S

tatio

n)

4.14

41.

261

0.24

60.

111

0.08

5−0

.170

0.

117

01

51.

893

Ave

rage

2.

529

0.53

10.

370

0.32

5−0

.385

−0.3

47

−0.1

50

Stan

dard

Dev

iatio

n 1.

615

0.44

30.

651

0.47

90.

950

0.86

1 2.

036

Panh

andl

e C

omm

unity

Ser

vice

s (A

mar

illo)

0.

461

2.34

6−0

.986

0.26

3−0

.751

−0.2

61

−0.4

830

06

0.00

0K

aufm

an C

ount

y Se

nior

Citi

zens

Ser

vice

(T

erre

ll)

−0.4

41−0

.704

1.68

9−0

.744

−0.6

46−0

.605

−0

.166

01

70.

321

Cle

burn

e (C

lebu

rne)

−0

.310

−0.7

263.

385

−0.8

05−0

.820

−0.7

27

−0.0

830

17

0.41

3G

ulf C

oast

Cen

ter (

Gal

vest

on)

−0.3

13−0

.621

0.82

8−0

.317

1.44

4−1

.293

0.

000

01

70.

472

Publ

ic T

rans

it Se

rvic

es (M

iner

al W

ells

) −0

.218

−0.4

740.

096

−0.0

72−0

.751

−0.6

86

−0.3

500

17

0.51

9C

omm

unity

Ser

vice

s, In

c. (C

orsi

cana

) −0

.104

−0.5

770.

986

−0.3

17−0

.124

−0.3

93

0.08

40

17

0.58

8Se

nior

Cen

ter R

esou

rces

and

Pub

lic T

rans

it −0

.481

−0.7

101.

648

−0.0

72−0

.298

−0.3

83

0.21

70

17

0.77

2C

ollin

Cou

nty

Com

mitt

ee o

n A

ging

(M

cKin

ney)

−0

.609

−0.7

291.

358

−1.5

99−1

.203

−1.4

45

−0.8

170

17

1.20

1Te

xom

a A

rea

Para

trans

it Sy

stem

(TA

PS)

(She

rman

) 0.

315

−0.1

26−0

.118

0.47

7−0

.786

−0.5

55

−0.3

160

17

1.31

8

117

Rur

al A

genc

y Po

p.L

and

Are

aD

ensi

ty%

65

+

% H

Hs

with

Z

ero

Aut

os

% b

elow

Po

vert

y L

evel

% A

ges

21–6

4 D

isab

led

Bor

der

Met

ro

Reg

ion

Clu

ster

N

o.

Dis

tanc

e to

C

ente

r of

C

lust

erSe

rvic

es P

rogr

am fo

r Agi

ng N

eeds

(SPA

N)

(Den

ton)

−0

.571

−0.7

221.

406

−1.6

60−1

.552

−1.0

30

−1.1

170

17

1.39

0Fo

rt B

end

Cou

nty

−0.7

29−0

.722

0.35

8−1

.965

−1.3

08−1

.405

−0

.833

01

71.

622

The

Tran

sit S

yste

m, I

nc. (

Gle

n R

ose)

−0

.453

−0.6

060.

263

1.30

1−1

.552

−0.7

88

−0.5

660

17

1.85

1

Ave

rage

−0

.356

−0.6

111.

082

−0.5

25−0

.691

−0.8

46

−0.3

59

Stan

dard

Dev

iatio

n 0.

286

0.18

00.

993

0.97

30.

848

0.38

9 0.

427

Kle

berg

Cou

nty

Hum

an S

ervi

ces

(Kin

gsvi

lle)

−0.7

67−0

.528

−0.8

24−0

.622

1.75

7−0

.535

−0

.383

10

80.

804

Low

er R

io G

rand

e V

alle

y D

evel

opm

ent

Cou

ncil

(McA

llen)

−0

.185

−0.4

900.

221

−0.3

171.

165

1.07

4 −0

.066

10

81.

131

Del

Rio

(Del

Rio

) −0

.684

−0.4

25−0

.811

−0.6

520.

364

0.96

3 −2

.834

10

81.

752

Ave

rage

−0

.545

−0.4

81−0

.471

−0.5

301.

095

0.50

1 −1

.094

Stan

dard

Dev

iatio

n 0.

315

0.05

20.

600

0.18

60.

699

0.89

8 1.

515

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

C

lust

er

1 2

34

56

78

1

5.56

45.

294

5.88

95.

532

3.40

66.

238

5.40

8 2

5.56

4

4.17

3.57

75.

227

4.93

44.

169

2.42

8 3

5.29

4 4.

17

5.47

43.

211

2.52

72.

578

3.48

4

5.88

9 3.

577

5.47

4

6.35

65.

999

6.18

75.

501

5 5.

532

5.22

73.

211

6.35

6

3.16

83.

269

4.85

1 6

3.40

6 4.

934

2.52

75.

999

3.16

8

3.78

44.

272

7 6.

238

4.16

92.

578

6.18

73.

269

3.78

4

3.82

0 8

5.40

8 2.

428

3.48

05.

501

4.85

14.

272

3.82

0

119

APP

EN

DIX

B. A

LT

ER

NA

TIV

E U

RB

AN

CL

UST

ER

AN

AL

YSI

S D

AT

A D

ET

AIL

Tab

le B

1. F

our

Clu

ster

s.

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

City

of A

bile

ne,

Texa

s U

ZA

−0.1

36

−0.4

52

1.10

7−0

.225

−0.6

940.

210

−0.3

68−0

.058

0.98

4−0

.774

00

12.

124

City

of B

row

nsvi

lle

UZA

0.

608

−0.2

57

2.55

41.

355

1.24

9−0

.759

2.47

5−0

.743

0.42

61.

195

10

13.

830

City

of H

arlin

gen

city

lim

its

−0.7

56

−0.7

24

−0.1

25−0

.086

0.67

21.

148

0.86

20.

205

0.92

2−0

.685

10

13.

069

City

Tra

nsit

Man

agem

ent

Com

pany

, Inc

.—Lu

bboc

k ci

ty li

mits

1.

031

0.90

3 −0

.018

−0.0

86−0

.542

−0.1

650.

002

0.26

3−0

.101

−0.9

240

01

2.49

2G

ulf C

oast

Cen

ter/

Con

nect

Tra

nsit—

Lake

Jack

son/

A

ngle

ton

UZA

−0

.554

−0

.730

0.

960

−0.2

71−0

.694

−0.7

59−0

.818

−0.0

43−1

.093

0.65

80

11

3.36

5

Hill

Cou

ntry

Tra

nsit

Dis

trict

—K

illee

n D

ivis

ion

city

lim

its

of K

illee

n,

Cop

pera

s C

ove,

H

arke

r H

eigh

ts

0.20

8 −0

.162

0.

927

0.19

3−0

.998

−2.0

71−0

.871

−0.6

410.

984

−0.2

970

01

2.69

6La

redo

Tra

nsit

Man

agem

ent

Inco

rpor

ated

ci

ty li

mits

0.

745

0.17

1 1.

124

0.56

51.

006

−1.1

651.

483

−0.7

140.

085

0.33

01

01

2.52

8

Ave

rage

0.

164

−0.1

79

0.93

30.

207

0.00

0−0

.509

0.39

5−0

.247

0.31

5−0

.071

Stan

dard

Dev

iatio

n 0.

676

0.57

4 0.

887

0.58

20.

938

1.03

11.

261

0.44

00.

762

0.81

1

Hid

algo

Cou

nty

com

bine

d (M

cAlle

n)

urba

nize

d H

idal

go

Cou

nty

and

McA

llen

Expr

ess

4.09

1 4.

384

−0.4

600.

597

0.22

2−0

.274

1.94

2−0

.633

0.32

80.

078

10

20.

000

Bra

zos T

rans

it D

istri

ct—

Bry

an/C

olle

ge

Stat

ion

city

lim

its

0.20

6 0.

274

−0.3

19−2

.223

−0.5

42−1

.603

1.45

71.

022

−0.0

39−1

.162

00

33.

475

Bra

zos T

rans

it D

istri

ct—

The

Woo

dlan

ds

desi

gnat

ed

plac

e bo

unda

ry

−0.7

79

−0.9

40

1.42

0−2

.688

−1.5

74−1

.290

−1.8

763.

924

−2.9

22−2

.655

01

33.

471

Col

lin C

ount

y A

rea

Re g

iona

l Tra

nsit

UZA

−0

.790

−0

.865

0.

620

−1.9

44−1

.423

−1.5

09−1

.373

1.70

7−1

.589

−1.2

820

13

1.35

9

Den

ton

Cou

nty

Tran

spor

tatio

n A

utho

rity

city

lim

its

of D

ento

n,

Hig

hlan

d V

illag

e,

Lew

isvi

lle

0.66

3 0.

681

−0.2

36−1

.247

−1.1

80−1

.759

−1.0

820.

803

−1.1

55−1

.103

01

32.

101

Ave

rage

−0

.175

−0

.213

0.

371

−2.0

26−1

.180

−1.5

40−0

.719

1.86

4−1

.426

−1.5

50

Stan

dard

Dev

iatio

n 0.

728

0.81

4 0.

818

0.60

30.

455

0.19

61.

487

1.42

61.

192

0.74

0

120

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

upat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

Bea

umon

t Mun

icip

al

Tran

sit

city

lim

its

−0.0

47

0.30

2 −0

.903

0.84

41.

037

0.55

40.

161

0.00

10.

457

−0.1

770

04

1.38

6C

ity o

f Am

arill

o—A

mar

illo

City

Tra

nsit

city

lim

its

0.70

4 0.

401

0.40

90.

007

−0.6

940.

335

−0.5

14−0

.495

0.14

70.

270

00

41.

928

City

of G

alve

ston

ci

ty li

mits

−0

.761

−0

.480

−1

.129

0.61

22.

676

0.55

40.

518

0.40

92.

131

−1.5

500

14

4.40

2

City

of P

ort A

rthur

ci

ty li

mits

−0

.754

0.

260

−2.3

311.

076

2.06

91.

304

0.90

1−1

.691

1.82

11.

673

00

43.

845

Con

cho

Val

ley

Tran

sit D

istri

ct

UZA

−0

.372

−0

.492

0.

412

0.10

0−0

.421

0.77

3−0

.368

−0.6

850.

705

−0.0

580

04

1.53

5G

olde

n C

resc

ent

Reg

iona

l Pla

nnin

g C

omm

issi

on—

Vic

toria

ci

t y li

mits

−0

.720

−0

.746

0.

191

0.00

7−0

.178

0.21

0−0

.487

−0.3

20−0

.411

0.36

00

04

1.49

5G

ulf C

oast

Cen

ter/

Con

nect

Tra

nsit—

Texa

s City

/La

Mar

que

UZA

−0

.267

−0

.230

−0

.222

0.56

5−0

.481

0.24

1−0

.580

−0.7

28−0

.318

0.47

90

14

2.42

7H

ill C

ount

ry T

rans

it D

istri

ct—

Tem

ple

Div

isio

n

city

lim

its

of T

empl

e an

d B

elto

n −0

.611

0.

157

−1.9

05−0

.178

0.39

90.

929

−0.4

870.

161

−0.3

181.

016

00

41.

915

Long

view

Tra

nsit

city

lim

its

−0.5

60

−0.3

09

−0.9

080.

426

−0.3

600.

616

−0.3

02−0

.305

−0.5

041.

046

00

41.

306

Mid

land

-Ode

ssa

Urb

an T

rans

it D

istri

ct

cit y

lim

its

0.86

3 0.

673

0.12

1−0

.783

−0.5

120.

116

−0.3

55−0

.101

−0.4

42−0

.386

00

42.

475

Texa

rkan

a U

rban

Tr

ansi

t Dis

trict

city

lim

its

of

Texa

rkan

a,

Nas

h,

Wak

e V

illag

e −0

.950

−0

.805

−0

.764

1.12

31.

067

1.17

90.

478

−0.1

45−0

.225

0.71

80

04

1.74

6Te

xom

a A

rea

Para

trans

it Sy

stem

, In

c.—

Sher

man

/Den

ison

U

ZA

−0.7

79

−0.7

69

0.00

80.

983

−0.1

781.

366

−0.6

06−0

.466

−0.5

351.

494

00

41.

985

Tyle

r Tra

nsit

city

lim

its

−0.4

25

−0.4

18

−0.1

000.

797

0.12

61.

085

−0.2

090.

001

−0.2

560.

718

00

41.

063

Wac

o Tr

ansi

t Sys

tem

AD

A

serv

ice

area

plu

s id

entif

iabl

e fe

atur

es

0.31

6 0.

163

0.18

20.

704

0.36

90.

523

0.61

0−0

.393

0.24

00.

628

00

41.

321

Wic

hita

Fal

ls T

rans

it S y

stem

ci

ty li

mits

−0

.173

0.

014

−0.6

14−0

.225

−0.4

210.

210

−0.5

93−0

.335

0.67

40.

390

00

41.

312

Ave

rage

−0

.302

−0

.152

−0

.504

0.40

40.

300

0.66

6−0

.122

−0.3

400.

211

0.44

1

Stan

dard

Dev

iatio

n 0.

551

0.46

8 0.

829

0.55

81.

005

0.42

00.

512

0.48

40.

830

0.79

4

121

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

Clu

ster

1

2 3

4

1 6.

611

4.73

02.

408

2 6.

611

8.

982

7.30

2

3 4.

730

8.98

2 5.

273

4 2.

408

7.30

2 5.

273

122

Tab

le B

2. F

ive

Clu

ster

s.

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

Bea

umon

t Mun

icip

al

Tran

sit

city

lim

its

−0.0

47

0.30

2 −0

.903

0.84

41.

037

0.55

40.

161

0.00

10.

457

−0.1

770

01

1.48

9

City

of P

ort A

rthur

ci

ty li

mits

−0

.754

0.

260

−2.3

311.

076

2.06

91.

304

0.90

1−1

.691

1.

821

1.67

30

01

3.33

3H

ill C

ount

ry T

rans

it D

istri

ct—

Tem

ple

Div

isio

n

city

lim

its

of T

empl

e an

d B

elto

n −0

.611

0.

157

−1.9

05−0

.178

0.39

90.

929

−0.4

870.

161

−0.3

181.

016

00

11.

690

Long

view

Tra

nsit

city

lim

its

−0.5

60

−0.3

09

−0.9

080.

426

−0.3

600.

616

−0.3

02−0

.305

−0

.504

1.04

60

01

1.26

2

Texa

rkan

a U

rban

Tr

ansi

t Dis

trict

city

lim

its

of

Texa

rkan

a,

Nas

h,

Wak

e V

illag

e −0

.950

−0

.805

−0

.764

1.12

31.

067

1.17

90.

478

−0.1

45

−0.2

250.

718

00

11.

195

Texo

ma

Are

a Pa

ratra

nsit

Syst

em,

Inc.

—Sh

erm

an/D

enis

on

UZA

−0

.779

−0

.769

0.

008

0.98

3−0

.178

1.36

6−0

.606

−0.4

66

−0.5

351.

494

00

11.

782

Tyle

r Tra

nsit

city

lim

its

−0.4

25

−0.4

18

−0.1

000.

797

0.12

61.

085

−0.2

090.

001

−0.2

560.

718

00

11.

085

Wac

o Tr

ansi

t Sys

tem

AD

A

serv

ice

area

plu

s id

entif

iabl

e fe

atur

es

0.31

6 0.

163

0.18

20.

704

0.36

90.

523

0.61

0−0

.393

0.

240

0.62

80

01

1.54

7

Ave

rage

−0

.476

−0

.177

−0

.840

0.72

20.

566

0.94

50.

068

−0.3

55

0.08

50.

890

Stan

dard

Dev

iatio

n 0.

420

0.45

8 0.

901

0.42

70.

793

0.34

20.

553

0.58

10.

782

0.57

1B

razo

s Tra

nsit

Dis

trict

—B

ryan

/Col

lege

St

atio

n ci

ty li

mits

0.

206

0.27

4 −0

.319

−2.2

23−0

.542

−1.6

031.

457

1.02

2−0

.039

−1.1

620

02

3.37

2C

ity o

f Abi

lene

, Te

xas

UZA

−0

.136

−0

.452

1.

107

−0.2

25−0

.694

0.21

0−0

.368

−0.0

58

0.98

4−0

.774

00

21.

748

City

of A

mar

illo—

Am

arill

o C

ity T

rans

it ci

ty li

mits

0.

704

0.40

1 0.

409

0.00

7−0

.694

0.33

5−0

.514

−0.4

95

0.14

70.

270

00

21.

385

City

Tra

nsit

Man

agem

ent

Com

pany

, Inc

.—Lu

bboc

k ci

ty li

mits

1.

031

0.90

3 −0

.018

−0.0

86−0

.542

−0.1

650.

002

0.26

3−0

.101

−0.9

240

02

1.72

0C

onch

o V

alle

y Tr

ansi

t Dis

trict

U

ZA

−0.3

72

−0.4

92

0.41

20.

100

−0.4

210.

773

−0.3

68−0

.685

0.

705

−0.0

580

02

1.80

6

Den

ton

Cou

nty

Tran

spor

tatio

n A

utho

rity

city

lim

its

of D

ento

n,

Hig

hlan

d V

illag

e,

Lew

isvi

lle

0.66

3 0.

681

−0.2

36−1

.247

−1.1

80−1

.759

−1.0

820.

803

−1.1

55−1

.103

01

23.

278

Gol

den

Cre

scen

t R

egio

nal P

lann

ing

Com

mis

sion

—V

icto

ria

cit y

lim

its

−0.7

20

−0.7

46

0.19

10.

007

−0.1

780.

210

−0.4

87−0

.320

−0

.411

0.36

00

02

1.68

1

123

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

Gul

f Coa

st C

ente

r/ C

onne

ct T

rans

it—La

ke

Jack

son/

An g

leto

n U

ZA

−0.5

54

−0.7

30

0.96

0−0

.271

−0.6

94−0

.759

−0.8

18−0

.043

−1

.093

0.65

80

12

2.66

6G

ulf C

oast

Cen

ter/

Con

nect

Tra

nsit—

Texa

s City

/La

Mar

que

UZA

−0

.267

−0

.230

−0

.222

0.56

5−0

.481

0.24

1−0

.580

−0.7

28

−0.3

180.

479

01

22.

423

Hill

Cou

ntry

Tra

nsit

Dis

trict

—K

illee

n D

ivis

ion

city

lim

its

of K

illee

n,

Cop

pera

s C

ove,

H

arke

r H

eigh

ts

0.20

8 −0

.162

0.

927

0.19

3−0

.998

−2.0

71−0

.871

−0.6

41

0.98

4−0

.297

00

22.

405

Mid

land

-Ode

ssa

Urb

an T

rans

it D

istri

ct

cit y

lim

its

0.86

3 0.

673

0.12

1−0

.783

−0.5

120.

116

−0.3

55−0

.101

−0

.442

−0.3

860

02

1.41

6W

ichi

ta F

alls

Tra

nsit

Syst

e m

city

lim

its

−0.1

73

0.01

4 −0

.614

−0.2

25−0

.421

0.21

0−0

.593

−0.3

35

0.67

40.

390

00

21.

564

Ave

rage

0.

121

0.01

1 0.

227

−0.3

49−0

.613

−0.3

55−0

.381

−0.1

10

−0.0

05−0

.212

Stan

dard

Dev

iatio

n 0.

583

0.57

1 0.

553

0.75

10.

268

0.95

10.

644

0.56

40.

733

0.65

6B

razo

s Tra

nsit

Dis

trict

—Th

e W

oodl

ands

desi

gnat

ed

plac

e bo

unda

ry

−0.7

79

−0.9

40

1.42

0−2

.688

−1.5

74−1

.290

−1.8

763.

924

−2.9

22−2

.655

01

31.

589

Col

lin C

ount

y A

rea

Reg

iona

l Tra

nsit

UZA

−0

.790

−0

.865

0.

620

−1.9

44−1

.423

−1.5

09−1

.373

1.70

7−1

.589

−1.2

820

13

1.58

9

Ave

rage

−0

.784

−0

.903

1.

020

−2.3

16−1

.498

−1.3

99−1

.625

2.81

6−2

.255

−1.9

68

Stan

dard

Dev

iatio

n 0.

008

0.05

3 0.

566

0.52

60.

107

0.15

50.

355

1.56

80.

942

0.97

1

City

of B

row

nsvi

lle

UZA

0.

608

−0.2

57

2.55

41.

355

1.24

9−0

.759

2.47

5−0

.743

0.

426

1.19

51

04

3.13

6

City

of G

alve

ston

ci

ty li

mits

−0

.761

−0

.480

−1

.129

0.61

22.

676

0.55

40.

518

0.40

92.

131

−1.5

500

14

4.21

2

City

of H

arlin

gen

city

lim

its

−0.7

56

−0.7

24

−0.1

25−0

.086

0.67

21.

148

0.86

20.

205

0.92

2−0

.685

10

42.

277

Lare

do T

rans

it M

anag

emen

t In

corp

orat

e d

city

lim

its

0.74

5 0.

171

1.12

40.

565

1.00

6−1

.165

1.48

3−0

.714

0.

085

0.33

01

04

2.13

5

Ave

rage

−0

.041

−0

.323

0.

606

0.61

21.

401

−0.0

561.

334

−0.2

11

0.89

1−0

.177

Stan

dard

Dev

iatio

n 0.

831

0.38

0 1.

593

0.58

90.

882

1.08

70.

859

0.60

40.

895

1.19

5

Hid

algo

Cou

nty

com

bine

d (M

cAlle

n)

urba

nize

d H

idal

go

Cou

nty

and

McA

llen

Expr

ess

4.09

1 4.

384

−0.4

600.

597

0.22

2−0

.274

1.94

2−0

.633

0.

328

0.07

81

05

0.00

0

124

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

Clu

ster

1

23

45

1 2.

757

7.44

43.

499

7.46

0

2 2.

757

5.43

43.

683

7.14

9

3 7.

444

5.43

47.

653

10.7

61

4 3.

499

3.68

37.

653

6.60

2

5 7.

460

7.14

910

.761

6.60

2

125

Tab

le B

3. S

ix C

lust

ers.

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

Hid

algo

Cou

nty

com

bine

d (M

cAlle

n)

urba

nize

d H

idal

go

Cou

nty

and

McA

llen

Expr

ess

4.09

1 4.

384

−0.4

600.

597

0.22

2−0

.274

1.94

2−0

.633

0.32

80.

078

10

10.

000

Bra

zos T

rans

it D

istri

ct—

Bry

an/C

olle

ge

Stat

ion

cit y

lim

its

0.20

6 0.

274

−0.3

19−2

.223

−0.5

42−1

.603

1.45

71.

022

−0.0

39−1

.162

00

23.

816

City

of A

bile

ne,

Texa

s U

ZA

−0.1

36

−0.4

52

1.10

7−0

.225

−0.6

940.

210

−0.3

68−0

.058

0.98

4−0

.774

00

21.

781

City

of A

mar

illo—

Am

arill

o C

ity T

rans

it ci

ty li

mits

0.

704

0.40

1 0.

409

0.00

7−0

.694

0.33

5−0

.514

−0.4

950.

147

0.27

00

02

1.15

2C

ity T

rans

it M

anag

emen

t C

ompa

ny, I

nc.—

Lubb

ock

city

lim

its

1.03

1 0.

903

−0.0

18−0

.086

−0.5

42−0

.165

0.00

20.

263

−0.1

01−0

.924

00

21.

979

Con

cho

Val

ley

Tran

sit D

istri

ct

UZA

−0

.372

−0

.492

0.

412

0.10

0−0

.421

0.77

3−0

.368

−0.6

850.

705

−0.0

580

02

1.29

9G

olde

n C

resc

ent

Reg

iona

l Pla

nnin

g C

omm

issi

on—

Vic

toria

ci

t y li

mits

−0

.720

−0

.746

0.

191

0.00

7−0

.178

0.21

0−0

.487

−0.3

20−0

.411

0.36

00

02

1.13

6G

ulf C

oast

Cen

ter/

Con

nect

Tra

nsit—

Lake

Ja

ckso

n/A

ngle

ton

UZA

−0

.554

−0

.730

0.

960

−0.2

71−0

.694

−0.7

59−0

.818

−0.0

43−1

.093

0.65

80

12

2.87

4G

ulf C

oast

Cen

ter/

Con

nect

Tra

nsit—

Texa

s City

/La

Mar

que

UZA

−0

.267

−0

.230

−0

.222

0.56

5−0

.481

0.24

1−0

.580

−0.7

28−0

.318

0.47

90

12

2.33

5

Hill

Cou

ntry

Tra

nsit

Dis

trict

—K

illee

n D

ivis

ion

city

lim

its

of K

illee

n,

Cop

pera

s C

ove,

H

arke

r H

eigh

ts

0.20

8 −0

.162

0.

927

0.19

3−0

.998

−2.0

71−0

.871

−0.6

410.

984

−0.2

970

02

2.73

4

Long

view

Tra

nsit

city

lim

its

−0.5

60

−0.3

09

−0.9

080.

426

−0.3

600.

616

−0.3

02−0

.305

−0.5

041.

046

00

21.

742

Mid

land

-Ode

ssa

Urb

an T

rans

it D

istri

ct

city

lim

its

0.86

3 0.

673

0.12

1−0

.783

−0.5

120.

116

−0.3

55−0

.101

−0.4

42−0

.386

00

21.

645

Texo

ma

Are

a Pa

ratra

nsit

Syst

em,

Inc.

—Sh

erm

an/D

enis

on

UZA

−0

.779

−0

.769

0.

008

0.98

3−0

.178

1.36

6−0

.606

−0.4

66−0

.535

1.49

40

02

2.45

2

Tyle

r Tra

nsit

city

lim

its

−0.4

25

−0.4

18

−0.1

000.

797

0.12

61.

085

−0.2

090.

001

−0.2

560.

718

00

21.

666

Wac

o Tr

ansi

t Sys

tem

AD

A

serv

ice

area

plu

s id

entif

iabl

e fe

atur

es

0.31

6 0.

163

0.18

20.

704

0.36

90.

523

0.61

0−0

.393

0.24

00.

628

00

21.

645

126

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

Wic

hita

Fal

ls T

rans

it Sy

stem

ci

ty li

mits

−0

.173

0.

014

−0.6

14−0

.225

−0.4

210.

210

−0.5

93−0

.335

0.67

40.

390

00

21.

184

Ave

rage

−0

.044

−0

.126

0.

142

−0.0

02−0

.415

0.07

3−0

.267

−0.2

190.

002

0.16

3

Stan

dard

Dev

iatio

n 0.

576

0.51

9 0.

566

0.77

40.

342

0.92

60.

594

0.44

30.

612

0.75

1B

razo

s Tra

nsit

Dis

trict

—Th

e W

oodl

ands

desi

gnat

ed

plac

e bo

unda

ry

−0.7

79

−0.9

40

1.42

0−2

.688

−1.5

74−1

.290

−1.8

763.

924

−2.9

22−2

.655

01

32.

684

Col

lin C

ount

y A

rea

Reg

iona

l Tra

nsit

UZA

−0

.790

−0

.865

0.

620

−1.9

44−1

.423

−1.5

09−1

.373

1.70

7−1

.589

−1.2

820

13

0.96

2

Den

ton

Cou

nty

Tran

spor

tatio

n A

utho

rity

city

lim

its

of D

ento

n,

Hig

hlan

d V

illag

e,

Lew

isvi

lle

0.66

3 0.

681

−0.2

36−1

.247

−1.1

80−1

.759

−1.0

820.

803

−1.1

55−1

.103

01

32.

482

Ave

rage

−0

.302

−0

.375

0.

602

−1.9

60−1

.392

−1.5

19−1

.444

2.14

5−1

.888

−1.6

80

Stan

dard

Dev

iatio

n 0.

835

0.91

5 0.

828

0.72

00.

199

0.23

50.

401

1.60

60.

921

0.84

9

City

of G

alve

ston

ci

ty li

mits

−0

.761

−0

.480

−1

.129

0.61

22.

676

0.55

40.

518

0.40

92.

131

−1.5

500

14

2.34

5

City

of H

arlin

gen

city

lim

its

−0.7

56

−0.7

24

−0.1

25−0

.086

0.67

21.

148

0.86

20.

205

0.92

2−0

.685

10

42.

345

Ave

rage

−0

.759

−0

.602

−0

.627

0.26

31.

674

0.85

10.

690

0.30

71.

526

−1.1

18

Stan

dard

Dev

iatio

n 0.

004

0.17

2 0.

710

0.49

31.

417

0.42

00.

243

0.14

40.

855

0.61

2B

eaum

ont M

unic

ipal

Tr

ansi

t ci

ty li

mits

−0

.047

0.

302

−0.9

030.

844

1.03

70.

554

0.16

10.

001

0.45

7−0

.177

00

51.

451

City

of P

ort A

rthur

ci

ty li

mits

−0

.754

0.

260

−2.3

311.

076

2.06

91.

304

0.90

1−1

.691

1.82

11.

673

00

52.

573

Hill

Cou

ntry

Tra

nsit

Dis

trict

—Te

mpl

e D

ivis

ion

city

lim

its

of T

empl

e an

d B

elto

n −0

.611

0.

157

−1.9

05−0

.178

0.39

90.

929

−0.4

870.

161

−0.3

181.

016

00

51.

755

Texa

rkan

a U

rban

Tr

ansi

t Dis

trict

city

lim

its

of

Texa

rkan

a,

Nas

h,

Wak

e V

illa g

e −0

.950

−0

.805

−0

.764

1.12

31.

067

1.17

90.

478

−0.1

45−0

.225

0.71

80

05

1.42

1

Ave

rage

−0

.591

−0

.022

−1

.476

0.71

61.

143

0.99

10.

263

−0.4

190.

434

0.80

7

Stan

dard

Dev

iatio

n 0.

388

0.52

6 0.

764

0.60

90.

690

0.33

10.

585

0.85

80.

987

0.76

8

City

of B

row

nsvi

lle

UZA

0.

608

−0.2

57

2.55

41.

355

1.24

9−0

.759

2.47

5−0

.743

0.42

61.

195

10

61.

112

Lare

do T

rans

it M

anag

emen

t In

corp

orat

ed

city

lim

its

0.74

5 0.

171

1.12

40.

565

1.00

6−1

.165

1.48

3−0

.714

0.08

50.

330

10

61.

112

Ave

rage

0.

677

−0.0

43

1.83

90.

960

1.12

8−0

.962

1.97

9−0

.728

0.25

60.

763

Stan

dard

Dev

iatio

n 0.

097

0.30

2 1.

011

0.55

90.

172

0.28

70.

701

0.02

10.

241

0.61

2

127

Dis

tanc

es b

etw

een

Fina

l Clu

ster

Cen

ters

Clu

ster

1

2 3

4 5

6

1

7.20

2 9.

729

7.78

7 7.

493

6.20

1

2 7.

202

5.

094

3.77

9 2.

782

4.64

1

3 9.

729

5.09

4

6.71

3 7.

160

7.87

6

4 7.

787

3.77

9 6.

713

3.

260

4.88

3

5 7.

493

2.78

2 7.

160

3.26

0

5.24

1

6 6.

201

4.64

1 7.

876

4.88

3 5.

241

128

Tab

le B

4. S

even

Clu

ster

s.

Nam

e

Serv

ice

Are

a D

efin

ition

Po

p.

Lan

d A

rea

Den

sity

% A

ges

21–6

4 D

isab

led

% H

Hs

with

Z

ero

Aut

os%

Pop

. 65

+

%

belo

w

Pove

rty

Lev

el

%

Man

agem

ent,

Prof

essi

onal

, an

d R

elat

ed

Occ

u pat

ions

%

Ser

vice

O

ccup

atio

ns

% P

rodu

ctio

n,

Tra

nspo

rtat

ion,

an

d M

ater

ial

Mov

ing

Occ

upat

ions

Bor

der

Met

roC

lust

er

No.

Dis

tanc

e to

Cen

ter

of E

ach

Clu

ster

City

of P

ort A

rthur

ci

ty li

mits

−0

.754

0.

260

−2.3

311.

076

2.06

91.

304

0.90

1−1

.691

1.82

11.

673

00

12.

336

Hill

Cou

ntry

Tra

nsit

Dis

trict

—Te

mpl

e D

ivis

ion

city

lim

its

of T

empl

e an

d B

elto

n −0

.611

0.

157

−1.9

05−0

.178

0.39

90.

929

−0.4

870.

161

−0.3

181.

016

00

11.

801

Texa

rkan

a U

rban

Tr

ansi

t Dis

trict

city

lim

its

of

Texa

rkan

a,

Nas

h,

Wak

e V

illag

e −0

.950

−0

.805

−0

.764

1.12

31.

067

1.17

90.

478

−0.1

45−0

.225

0.71

80

01

1.52

3

Ave

rage

−0

.772

−0

.130

−1

.667

0.67

41.

178

1.13

70.

297

−0.5

580.

426

1.13

6

Stan

dard

Dev

iatio

n 0.

170

0.58

7 0.

810

0.73

80.

840

0.19

10.

712

0.99

31.

209

0.48

9B

razo

s Tra

nsit

Dis

trict

—B

ryan

/Col

lege

St

atio

n ci

t y li

mits

0.

206

0.27

4 −0

.319

−2.2

23−0

.542

−1.6

031.

457

1.02

2−0

.039

−1.1

620

02

2.77

2C

ity T

rans

it M

anag

emen

t C

ompa

ny, I

nc.—

Lubb

ock

city

lim

its

1.03

1 0.

903

−0.0

18−0

.086

−0.5

42−0

.165

0.00

20.

263

−0.1

01−0

.924

00

21.

733

Den

ton

Cou

nty

Tran

spor

tatio

n A

utho

rity

city

lim

its

of D

ento

n,

Hig

hlan

d V

illag

e,

Lew

isvi

lle

0.66

3 0.

681

−0.2

36−1

.247

−1.1

80−1

.759

−1.0

820.

803

−1.1

55−1

.103

01

22.

455

Gul

f Coa

st C

ente

r/ C

onne

ct T

rans

it—La

ke

Jack

son/

Ang

leto

n U

ZA

−0.5

54

−0.7

30

0.96

0−0

.271

−0.6

94−0

.759

−0.8

18−0

.043

−1.0

930.

658

01

22.

777

Hill

Cou

ntry

Tra

nsit

Dis

trict

—K

illee

n D

ivis

ion

city

lim

its

of K

illee

n,

Cop

pera

s C

ove,

H

arke

r H

eigh

ts

0.20

8 −0

.162

0.

927

0.19

3−0

.998

−2.0

71−0

.871

−0.6

410.

984

−0.2

970

02

2.47

9M

idla

nd-O

dess

a U

rban

Tra

nsit

Dis

trict

ci

ty li

mits

0.

863

0.67

3 0.

121

−0.7

83−0

.512

0.11

6−0

.355

−0.1

01−0

.442

−0.3

860

02

1.60

3

Ave

rage

0.

403

0.27

3 0.

239

−0.7

36−0

.745

−1.0

40−0

.278

0.21

7−0

.307

−0.5

36

Stan

dard

Dev

iatio

n 0.

578

0.61

9 0.

567

0.89

20.

280

0.90

30.

936

0.61

60.

792

0.68

8B

razo

s Tra

nsit

Dis

trict

—Th

e W

oodl

ands

desi

gnat

ed

plac

e bo

unda

ry

−0.7

79

−0.9

40

1.42

0−2

.688

−1.5

74−1

.290

−1.8

763.

924

−2.9

22−2

.655

01

31.

589

Col

lin C

ount

y A

rea

Reg

iona

l Tra

nsit

UZA

−0

.790

−0

.865

0.

620

−1.9

44−1

.423

−1.5

09−1

.373

1.70

7−1

.589

−1.2

820

13

1.58

9

Ave

rage

−0

.784

−0

.903

1.

020

−2.3

16−1

.498

−1.3

99−1

.625

2.81

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131

APPENDIX C. EFFECTIVENESS AND EFFICIENCY MEASURES BY TRANSIT DISTRICT

Table C1. Rural Transit District Effectiveness and Efficiency Measures (Fiscal Year 2009).

Rural Transit Districts



Mile State Average for Rural Transit Districts 0.39 0.17Alamo Area Council of Governments (San Antonio) 0.41 0.09 Ark-Tex Council of Governments (Texarkana) 0.55 0.30 Aspermont Small Business Development Center 0.45 0.04 Bee Community Action Agency (Beeville) 0.38 0.12 Brazos Transit—The District (Bryan) 0.26 0.29 Capital Area Rural Transportation System (CARTS) (Austin) 0.43 0.19 Caprock Community Action Association (Crosbyton) 0.43 0.15 Central Texas Rural Transit District (Coleman) 0.42 0.12 Cleburne (Cleburne) 0.29 0.14 Collin County Area Regional Transit (McKinney) 0.60 0.11 Colorado Valley Transit (Columbus) 0.36 0.15 Community Act. Council of South Texas 0.21 0.38 Community Council of Southwest Texas (Uvalde) 0.46 0.13 Community Services, Inc. (Corsicana) 0.41 0.20 Concho Valley Transit District (Rural) 0.20 0.23 Del Rio 0.33 0.25 East Texas Council of Governments (Kilgore) 0.33 0.09 El Paso County 0.38 0.26 Fort Bend County 0.40 0.21 Golden Crescent Regional Planning Commission 0.53 0.13 Gulf Coast Center (Galveston) 0.26 0.10 Heart of Texas Council of Governments (Waco) 0.53 0.09 Hill Country Transit District (San Saba) 0.36 0.20 Kaufman Area Rural Transportation 0.48 0.15 Kleberg County Human Services 0.24 0.26 Lower Rio Grande Valley Development Council 0.41 0.14 Panhandle Community Services (Amarillo) 0.38 0.30 Public Transit Services (Mineral Wells) 0.63 0.10 Rolling Plains Management Corp. (Crowell) 0.43 0.19 Rural Economic Assistance League, Inc. 0.46 0.31 Senior Center Resources and Public Transit Service 0.48 0.13 Services Program for Aging Needs (SPAN) 0.37 0.11 South East Texas Regional Planning Commission 0.23 0.15 South Plains Community Action Association (Levelland) 0.33 0.13 Texoma Area Paratransit System/TAPS (Sherman) 0.44 0.13 The Transit System, Inc. (Glen Rose) 0.28 0.11 Webb County Community Action Agency (Laredo) 0.32 0.38 West Texas Opportunities, Inc. (Lamesa) 0.39 0.08 Not Included in Rural Average South Padre Island (South Padre Island) 0.41 1.45

132

Table C2. Urban Transit District Effectiveness and Efficiency Measures (Fiscal Year 2009).

Urban Transit Districts



Mile State Average for Urban Transit Districts 0.27 0.70Abilene 0.35 0.65 Amarillo 0.23 0.40 Beaumont 0.20 0.70 Brownsville 0.15 1.69 College Station-Bryan 0.28 1.95 Galveston 0.12 1.63 Harlingen-San Benito 0.20 0.10 Killeen-Copperas Cove-Harker Heights 0.30 0.34 Lake Jackson-Angleton 0.23 0.08 Laredo 0.16 2.06 Longview 0.25 0.57 Lubbock 0.25 1.19 McAllen 0.28 0.53 McKinney 0.42 0.21 Midland-Odessa 0.26 0.51 Port Arthur 0.16 0.37 San Angelo 0.37 0.41 Sherman-Denison 0.62 0.25 Temple 0.28 0.28 Texarkana 0.27 0.75 Texas City-LaMarque 0.30 0.13 The Woodlands 0.20 0.99 Tyler 0.23 0.63 Victoria 0.38 0.45 Waco 0.25 0.60 Wichita Falls 0.37 0.63 Average for Limited Eligibility Urban Transit Districts 0.33 0.17 Grand Prairie 0.24 0.30 Mesquite 0.40 0.15 Arlington 0.26 0.16 NETS 0.43 0.09

133

APPENDIX D. CASE STUDY FACT-FINDING QUESTIONS

Transit Environment: 1. Organization type

a. Council of governments/regional planning commission b. Community service agency c. Transit agency d. City/county

2. Number of counties and/or cities served 3. Square miles/population (PTN-128) 4. How is the economy of the area served (loss of major employers, economic impacts to

transit service)? 5. What are the major transit service generators (hospitals, schools, employment centers,

etc.)? 6. What other environmental factors or influences (roadway networks, natural barriers—

borders, lakes, mountains)?

Service Design/Delivery: 1. Number of annual passenger boardings in fiscal year 2009 (PTN-128) 2. Total number of fixed-route passenger boardings in fiscal year 2009:

Fixed Route Type No. of Passenger

BoardingsLocal

Commuter Feeder Other Total

3. Number of demand response passenger boardings in fiscal year 2009

Demand Response Type No. of Passenger Boardings

Advanced reservation (non-subscription) Same day Subscription (please describe) Other (please describe) Total

134

4. Does your transit system group all trip types, or do some trip types have dedicated vehicles (MTP, schools, etc.)?

Passenger Boardings by Directly Operated or Contracted

No. of Passenger Boardings Directly Operated Contracted

General public Clientele-type services Total

5. Approximately how many spare vehicles does your transit agency have on an average

day? 6. What fleet issues are you having, if any, that may affect the efficiency and effectiveness

of the transit system? 7. Please list the technology products used in the table below:

Technology Type Please List the Product in Use in Fiscal Year 2009

Computer-assisted scheduling and dispatching/automated scheduling and routing software

Automated vehicle locators Mobile data terminals/computers Other communication equipment (cell phones, radios)

Electronic payment systems Interactive voice response Other mapping technologies (e.g., online mapping tools)

8. Does the agency provide ongoing dispatch training in the use of technologies? How

often? 9. If it uses computer-assisted scheduling and dispatching (CASD) or automated scheduling,

does the agency re-optimize the CASD parameters to test productivity levels periodically?

10. Does the agency use automated vehicle locators to find the closest vehicle to a waiting patron, to provide vehicle updates to waiting patrons, to determine if the driver is in the right place when calling in a no-show, or to track driver whereabouts?

11. Does the agency use mobile data terminals to log arrival and departure times of vehicles, request permission for no-shows, or monitor drivers that may be off route?

12. Does the agency use technology to minimize driver trip times (review trip lengths, speeds, non-productive time)?

13. Is service coordinated with other transit agencies to utilize vehicle resources during times of day or in areas where service demand is low?

14. Where are vehicles located—end of the day or during the day? How is deadhead managed to be minimized?

135

15. Does the agency educate patrons on the policies and procedures (fares, cancellations, pick-up windows, shared-ride service)?

16. What service reports does the agency generate? How often and how are they used (is a copy available)?

Report Type

Please Indicate Yes If Generated

How Often Generated (Monthly/Annually)? How Is the Report Used?

Productivity reports No-shows/cancellations Late/missed trips Grouping trips Slack (open times in schedules) Calls—staffing, service quality Fleet reliability/ monitoring/servicing

Complaints Driver issues (unreliable drivers, lost drivers, etc.)

17. Describe the reservation, schedule, and day-of-service delivery (demand response):

Process Description Patron calls in… (appointment or pick-up time scheduled?)

How is the reservation recorded (manual, scheduling system)?

Is the patron given a pick-up window? Scheduler optimizes schedules at the end of the day?

How are patrons informed of changes to the schedule (within pick-up window)?

When are driver manifests printed, and how are they distributed?

What role does the driver have in service delivery (change schedule)?

How do dispatchers monitor for late pull-outs, missed trips, cancellations, no-shows (trouble dispatcher)?

How do drivers communicate or record pick-ups and drop-offs to dispatch?

When do drivers drop off manifests? Are manifests reviewed for completeness?

136

Service Policies/Procedures: Policy/Procedure Description Door-through-door, door-to-door, or curb-to-curb service? Allow will-call trips? Average on-hold, telephone queue time for reservations? Is there a subscription requirement? Is there an eligibility requirement? Does the agency have a send-back policy? Does the agency have a no-show/cancellation policy? Does the agency have an extra-board for drivers? Does the agency have a vehicle breakdown/accident procedure?

Does the agency have dwell time/idle time and pick-up window policies?

Cost Factors:

Cost Factor Question Description Fuel How does the agency purchase fuel?

What is the percent of budget that is fuel?

Full-Time Drivers

No. of full-time drivers? Beginning wage, average wage of full-time, highest wage?

Guarantee 40 hours per week pay? Average overtime per week per driver?

Part-Time Drivers

No. of part-time drivers? Beginning wage, average wage of full-time, highest wage?

Guaranteed pay hours per week? How much? Health Benefit Amount of health benefit paid by agency? Life and/or Other Benefits

Amount of life and/or other benefits paid by agency?

Maintenance Cost

What is the amount of maintenance cost (preventive maintenance, major repair, body work, etc.) in fiscal year 2009?

Contract Service

If the agency contracts service, what are the contract terms (cost per mile, cost per passenger, etc.)?

Other Staff No. of dispatchers/schedulers/reservations (full-time and part-time)

No. of supervisors No. of mechanics No. of administrative staff Other overhead staff allocated to transit

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